In [1]:
rm(list=ls())
gc()
setwd("/hpc/group/pbenfeylab/CheWei/CW_data/genesys/")
| used | (Mb) | gc trigger | (Mb) | max used | (Mb) | |
|---|---|---|---|---|---|---|
| Ncells | 625293 | 33.4 | 1361470 | 72.8 | 1361470 | 72.8 |
| Vcells | 1159120 | 8.9 | 8388608 | 64.0 | 1802279 | 13.8 |
In [2]:
## Need seu4
suppressMessages(library(Seurat))
suppressMessages(library(cowplot))
suppressMessages(library(scattermore))
suppressMessages(library(scater))
suppressMessages(library(cowplot))
suppressMessages(library(RColorBrewer))
suppressMessages(library(grid))
suppressMessages(library(gplots))
suppressMessages(library(circular))
suppressMessages(library(ggplot2))
suppressMessages(library(ggnewscale))
suppressMessages(library(tidyverse))
suppressMessages(library(ComplexHeatmap))
suppressMessages(library(circlize))
suppressMessages(library(patchwork))
In [3]:
sessionInfo()
R version 4.2.2 (2022-10-31) Platform: x86_64-conda-linux-gnu (64-bit) Running under: AlmaLinux 9.3 (Shamrock Pampas Cat) Matrix products: default BLAS/LAPACK: /hpc/group/pbenfeylab/ch416/miniconda3/envs/seu4/lib/libopenblasp-r0.3.21.so locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=en_US.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] grid stats4 stats graphics grDevices utils datasets [8] methods base other attached packages: [1] patchwork_1.1.3 circlize_0.4.15 [3] ComplexHeatmap_2.14.0 forcats_0.5.2 [5] stringr_1.5.1 dplyr_1.1.3 [7] purrr_1.0.2 readr_2.1.3 [9] tidyr_1.3.0 tibble_3.2.1 [11] tidyverse_1.3.2 ggnewscale_0.4.8 [13] circular_0.4-95 gplots_3.1.3 [15] RColorBrewer_1.1-3 scater_1.26.1 [17] ggplot2_3.4.4 scuttle_1.8.0 [19] SingleCellExperiment_1.20.0 SummarizedExperiment_1.28.0 [21] Biobase_2.58.0 GenomicRanges_1.50.0 [23] GenomeInfoDb_1.34.8 IRanges_2.32.0 [25] S4Vectors_0.36.0 BiocGenerics_0.44.0 [27] MatrixGenerics_1.10.0 matrixStats_1.1.0 [29] scattermore_1.2 cowplot_1.1.1 [31] SeuratObject_4.1.3 Seurat_4.1.1.9001 loaded via a namespace (and not attached): [1] utf8_1.2.4 spatstat.explore_3.2-5 [3] reticulate_1.34.0 tidyselect_1.2.0 [5] htmlwidgets_1.6.2 BiocParallel_1.32.5 [7] Rtsne_0.16 munsell_0.5.0 [9] ScaledMatrix_1.6.0 codetools_0.2-19 [11] ica_1.0-3 pbdZMQ_0.3-8 [13] future_1.33.0 miniUI_0.1.1.1 [15] withr_2.5.2 spatstat.random_3.2-1 [17] colorspace_2.1-0 progressr_0.14.0 [19] uuid_1.1-0 ROCR_1.0-11 [21] tensor_1.5 listenv_0.9.0 [23] repr_1.1.4 GenomeInfoDbData_1.2.9 [25] polyclip_1.10-6 parallelly_1.36.0 [27] vctrs_0.6.4 generics_0.1.3 [29] timechange_0.1.1 doParallel_1.0.17 [31] R6_2.5.1 clue_0.3-64 [33] ggbeeswarm_0.7.1 rsvd_1.0.5 [35] bitops_1.0-7 spatstat.utils_3.0-4 [37] DelayedArray_0.24.0 assertthat_0.2.1 [39] promises_1.2.1 scales_1.2.1 [41] googlesheets4_1.0.1 beeswarm_0.4.0 [43] gtable_0.3.4 beachmat_2.14.0 [45] globals_0.16.2 goftest_1.2-3 [47] rlang_1.1.2 GlobalOptions_0.1.2 [49] splines_4.2.2 lazyeval_0.2.2 [51] gargle_1.2.1 spatstat.geom_3.2-7 [53] broom_1.0.2 modelr_0.1.10 [55] reshape2_1.4.4 abind_1.4-5 [57] backports_1.4.1 httpuv_1.6.12 [59] tools_4.2.2 ellipsis_0.3.2 [61] ggridges_0.5.4 Rcpp_1.0.11 [63] plyr_1.8.9 base64enc_0.1-3 [65] sparseMatrixStats_1.10.0 zlibbioc_1.44.0 [67] RCurl_1.98-1.6 deldir_1.0-9 [69] GetoptLong_1.0.5 pbapply_1.7-2 [71] viridis_0.6.4 zoo_1.8-12 [73] haven_2.5.1 ggrepel_0.9.4 [75] cluster_2.1.4 fs_1.6.3 [77] magrittr_2.0.3 data.table_1.14.8 [79] RSpectra_0.16-1 reprex_2.0.2 [81] lmtest_0.9-40 RANN_2.6.1 [83] googledrive_2.0.0 mvtnorm_1.1-3 [85] fitdistrplus_1.1-11 hms_1.1.2 [87] mime_0.12 evaluate_0.23 [89] xtable_1.8-4 readxl_1.4.1 [91] shape_1.4.6 fastDummies_1.7.3 [93] gridExtra_2.3 compiler_4.2.2 [95] KernSmooth_2.23-20 crayon_1.5.2 [97] htmltools_0.5.7 tzdb_0.3.0 [99] later_1.3.1 lubridate_1.9.0 [101] DBI_1.1.3 dbplyr_2.2.1 [103] MASS_7.3-58.3 boot_1.3-28.1 [105] Matrix_1.5-4 cli_3.6.1 [107] parallel_4.2.2 igraph_1.5.1 [109] pkgconfig_2.0.3 sp_2.1-1 [111] IRdisplay_1.1 plotly_4.10.3 [113] spatstat.sparse_3.0-3 foreach_1.5.2 [115] xml2_1.3.3 vipor_0.4.5 [117] XVector_0.38.0 rvest_1.0.3 [119] digest_0.6.33 sctransform_0.4.1 [121] RcppAnnoy_0.0.21 spatstat.data_3.0-3 [123] cellranger_1.1.0 leiden_0.4.3 [125] uwot_0.1.16 DelayedMatrixStats_1.20.0 [127] shiny_1.7.5.1 gtools_3.9.4 [129] rjson_0.2.21 lifecycle_1.0.4 [131] nlme_3.1-162 jsonlite_1.8.7 [133] BiocNeighbors_1.16.0 viridisLite_0.4.2 [135] fansi_1.0.5 pillar_1.9.0 [137] lattice_0.21-8 fastmap_1.1.1 [139] httr_1.4.7 survival_3.4-0 [141] glue_1.6.2 iterators_1.0.14 [143] png_0.1-8 stringi_1.8.1 [145] RcppHNSW_0.5.0 BiocSingular_1.14.0 [147] caTools_1.18.2 IRkernel_1.3.1.9000 [149] irlba_2.3.5.1 future.apply_1.11.0
Load TF list¶
In [4]:
wanted_TFs <- read.csv("./Kay_TF_thalemine_annotations.csv")
In [5]:
nrow(wanted_TFs)
2484
In [6]:
## Make TF names unique
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G33880"]="WOX9"
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G45160"]="SCL27"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G04410"]="NAC78"
wanted_TFs$Name[wanted_TFs$GeneID=="AT3G29035"]="ORS1"
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G02540"]="ZHD3"
wanted_TFs$Name[wanted_TFs$GeneID=="AT3G16500"]="IAA26"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G09740"]="HAG5"
wanted_TFs$Name[wanted_TFs$GeneID=="AT4G24660"]="ZHD2"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G46880"]="HDG5"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G28420"]="RLT1"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G14580"]="BLJ"
wanted_TFs$Name[wanted_TFs$GeneID=="AT3G45260"]="BIB"
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G02070"]="RVN"
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G28160"]="FIT"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G68360"]="GIS3"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G20640"]="NLP4"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G05550"]="VFP5"
wanted_TFs$Name[wanted_TFs$GeneID=="AT3G59470"]="FRF1"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G15150"]="HAT7"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G14750"]="WER"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G75710"]="BRON"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G74500"]="TMO7"
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G12646"]="RITF1"
wanted_TFs$Name[wanted_TFs$GeneID=="AT3G48100"]="ARR5"
wanted_TFs$Name[wanted_TFs$GeneID=="AT4G16141"]="GATA17L"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G65640"]="NFL"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G62700"]="VND5"
wanted_TFs$Name[wanted_TFs$GeneID=="AT4G36160"]="VND2"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G66300"]="VND3"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G12260"]="VND4"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G62380"]="VND6"
In [7]:
## TTG1
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G24520"]
'TTG1'
In [8]:
## SCRAMBLED
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G11130"]
In [9]:
## CAPRICE
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G46410"]
'CPC'
Load GRN centrality scores for each transition (LIMA) (GeneSys)¶
In [10]:
stem2pro <- read.csv("./TF_GRN_centrality_t0-t1_zscore3.csv")
pro2trans <- read.csv("./TF_GRN_centrality_t1-t3_zscore3.csv")
trans2el <- read.csv("./TF_GRN_centrality_t3-t5_zscore3.csv")
el2el <- read.csv("./TF_GRN_centrality_t5-t7_zscore3.csv")
el2mat <- read.csv("./TF_GRN_centrality_t7-t9_zscore3.csv")
In [11]:
head(stem2pro)
| X | tf_occurance | atri_degree_centrality | atri_out_centrality | atri_in_centrality | atri_betweenness_centrality | atri_closeness_centrality | atri_eigenvector_centrality | tri_degree_centrality | tri_out_centrality | ... | phl_in_centrality | phl_betweenness_centrality | phl_closeness_centrality | phl_eigenvector_centrality | col_degree_centrality | col_out_centrality | col_in_centrality | col_betweenness_centrality | col_closeness_centrality | col_eigenvector_centrality | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ... | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | H2AXA | 10 | 0.4023372 | 0.1969950 | 0.2053422 | 0.04084567 | 0.002893454 | 0.06930489 | 0.3385580 | 0.10658307 | ... | 0.20268757 | 0.1068750973 | 0.0011064411 | 0.04917093 | 0.3140625 | 0.2375000 | 0.0765625 | 0.4845462 | 0.003069275 | 0.05594356 |
| 2 | HB-2 | 10 | 1.0217028 | 0.2904841 | 0.7312187 | 0.88444230 | 0.003620554 | 0.10318643 | 0.2915361 | 0.03134796 | ... | 0.03471445 | 0.0006063604 | 0.0011005165 | 0.05188664 | 1.1687500 | 0.6250000 | 0.5437500 | 0.9751589 | 0.004531334 | 0.10857313 |
| 3 | GATA19 | 10 | 1.0200334 | 0.6327212 | 0.3873122 | 0.98922117 | 0.003523433 | 0.10154627 | 0.2539185 | 0.10344828 | ... | 0.13101904 | 0.4793435741 | 0.0009728508 | 0.04139889 | 0.2281250 | 0.0609375 | 0.1671875 | 0.9294601 | 0.003688327 | 0.04870697 |
| 4 | HTA10 | 10 | 0.4590985 | 0.2237062 | 0.2353923 | 0.56394716 | 0.002961137 | 0.07343545 | 0.3855799 | 0.12852665 | ... | 0.28891377 | 0.4712185961 | 0.0011567435 | 0.05680820 | 0.2781250 | 0.1593750 | 0.1187500 | 0.6063503 | 0.002957289 | 0.05189729 |
| 5 | GAMMA-H2AX | 10 | 0.5876461 | 0.3756260 | 0.2120200 | 0.87042786 | 0.003067845 | 0.07959582 | 0.8275862 | 0.50156740 | ... | 0.27547592 | 0.0141747724 | 0.0012103003 | 0.05417356 | 0.3062500 | 0.1546875 | 0.1515625 | 0.9231123 | 0.003441618 | 0.05485739 |
| 6 | GRP2B | 9 | 0.3405676 | 0.1903172 | 0.1502504 | 0.90321104 | 0.002998468 | 0.06278893 | 0.0000000 | 0.00000000 | ... | 0.20716685 | 0.9618758756 | 0.0011917313 | 0.08047918 | 0.5062500 | 0.4484375 | 0.0578125 | 0.4854582 | 0.003975758 | 0.08353683 |
In [12]:
head(pro2trans)
| X | tf_occurance | atri_degree_centrality | atri_out_centrality | atri_in_centrality | atri_betweenness_centrality | atri_closeness_centrality | atri_eigenvector_centrality | tri_degree_centrality | tri_out_centrality | ... | phl_in_centrality | phl_betweenness_centrality | phl_closeness_centrality | phl_eigenvector_centrality | col_degree_centrality | col_out_centrality | col_in_centrality | col_betweenness_centrality | col_closeness_centrality | col_eigenvector_centrality | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ... | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | H2AXA | 7 | 0.1624473 | 0.08227848 | 0.08016878 | 0.01555294 | 0.001103735 | 0.05928981 | 0.14465409 | 0.058700210 | ... | 0.21161049 | 0.000000000 | 0.0010778420 | 0.05035964 | 0.35972222 | 0.086111111 | 0.27361111 | 0.07714225 | 0.0007635986 | 0.08372615 |
| 2 | BME3 | 7 | 0.2616034 | 0.22995781 | 0.03164557 | 0.03877753 | 0.001300033 | 0.07647016 | 0.24528302 | 0.140461216 | ... | 0.03183521 | 0.641672112 | 0.0006751422 | 0.02687088 | 0.04722222 | 0.005555556 | 0.04166667 | 0.00000000 | 0.0006292341 | 0.02312157 |
| 3 | HAT1 | 7 | 0.3080169 | 0.15611814 | 0.15189873 | 0.83841803 | 0.001372900 | 0.08713217 | 0.07337526 | 0.008385744 | ... | 0.12921348 | 0.856219828 | 0.0007598532 | 0.04521189 | 0.13194444 | 0.034722222 | 0.09722222 | 0.80140434 | 0.0006157478 | 0.04761614 |
| 4 | MYB12 | 6 | 0.4324895 | 0.40717300 | 0.02531646 | 0.18455232 | 0.001544689 | 0.09978931 | 0.03563941 | 0.016771488 | ... | 0.00000000 | 0.000000000 | 0.0000000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.0000000000 | 0.00000000 |
| 5 | GRP2B | 6 | 0.2531646 | 0.14556962 | 0.10759494 | 0.04942418 | 0.001365611 | 0.07830556 | 0.00000000 | 0.000000000 | ... | 0.05430712 | 0.031413594 | 0.0010275356 | 0.08967945 | 0.08333333 | 0.029166667 | 0.05416667 | 0.42131819 | 0.0004438084 | 0.03616124 |
| 6 | GATA15 | 6 | 0.3797468 | 0.19620253 | 0.18354430 | 0.06227866 | 0.001610540 | 0.09551755 | 0.23270440 | 0.041928721 | ... | 0.02996255 | 0.002817772 | 0.0005445657 | 0.01997458 | 0.00000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.0000000000 | 0.00000000 |
In [13]:
head(trans2el)
| X | tf_occurance | atri_degree_centrality | atri_out_centrality | atri_in_centrality | atri_betweenness_centrality | atri_closeness_centrality | atri_eigenvector_centrality | tri_degree_centrality | tri_out_centrality | ... | phl_in_centrality | phl_betweenness_centrality | phl_closeness_centrality | phl_eigenvector_centrality | col_degree_centrality | col_out_centrality | col_in_centrality | col_betweenness_centrality | col_closeness_centrality | col_eigenvector_centrality | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ... | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | AT4G30410 | 6 | 0.7468354 | 0.60506329 | 0.1417722 | 0.856499390 | 0.0004675479 | 0.15472441 | 0.8629550 | 0.53319058 | ... | 0.00000000 | 0.0000000 | 0.0000000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.0000000 | 0.0000000000 | 0.00000000 |
| 2 | ATCTH | 6 | 0.3088608 | 0.05569620 | 0.2531646 | 0.002081861 | 0.0004639463 | 0.10061684 | 0.1605996 | 0.02569593 | ... | 0.00000000 | 0.0000000 | 0.0000000000 | 0.00000000 | 0.35532995 | 0.299492386 | 0.05583756 | 0.7610230 | 0.0010902563 | 0.08569533 |
| 3 | IAA14 | 5 | 0.3392405 | 0.25569620 | 0.0835443 | 0.009760329 | 0.0003937151 | 0.10527274 | 0.2312634 | 0.13918630 | ... | 0.00000000 | 0.0000000 | 0.0000000000 | 0.00000000 | 0.01649746 | 0.001269036 | 0.01522843 | 0.0000000 | 0.0007651091 | 0.01138349 |
| 4 | BIM1 | 5 | 0.5316456 | 0.31645570 | 0.2151899 | 0.011379554 | 0.0004630263 | 0.13175744 | 0.1070664 | 0.06209850 | ... | 0.07387141 | 0.0000000 | 0.0003685896 | 0.04403974 | 0.27918782 | 0.189086294 | 0.09010152 | 0.9445656 | 0.0012919391 | 0.07964298 |
| 5 | GATA4 | 5 | 0.2911392 | 0.04556962 | 0.2455696 | 0.001702757 | 0.0005350022 | 0.09326911 | 0.6295503 | 0.02783726 | ... | 0.00000000 | 0.0000000 | 0.0000000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.0000000 | 0.0000000000 | 0.00000000 |
| 6 | HAT2 | 5 | 0.0000000 | 0.00000000 | 0.0000000 | 0.000000000 | 0.0000000000 | 0.00000000 | 0.0000000 | 0.00000000 | ... | 0.07250342 | 0.9518112 | 0.0003552297 | 0.05053935 | 0.00000000 | 0.000000000 | 0.00000000 | 0.0000000 | 0.0000000000 | 0.00000000 |
In [14]:
head(el2el)
| X | tf_occurance | atri_degree_centrality | atri_out_centrality | atri_in_centrality | atri_betweenness_centrality | atri_closeness_centrality | atri_eigenvector_centrality | tri_degree_centrality | tri_out_centrality | ... | phl_in_centrality | phl_betweenness_centrality | phl_closeness_centrality | phl_eigenvector_centrality | col_degree_centrality | col_out_centrality | col_in_centrality | col_betweenness_centrality | col_closeness_centrality | col_eigenvector_centrality | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ... | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | RD26 | 7 | 0.6697248 | 0.43425076 | 0.23547401 | 0.145531979 | 0.0006467123 | 0.13050671 | 0.16901408 | 0.118309859 | ... | 0.004237288 | 0.004712759 | 0.0003913220 | 0.04770843 | 0.01111111 | 0.003174603 | 0.007936508 | 0.000000000 | 0.000717119 | 0.006922739 |
| 2 | HAT22 | 6 | 0.1345566 | 0.09174312 | 0.04281346 | 0.000000000 | 0.0004387974 | 0.04661033 | 0.53802817 | 0.400000000 | ... | 0.032485876 | 0.000000000 | 0.0002677743 | 0.02895724 | 0.08412698 | 0.019047619 | 0.065079365 | 0.001249148 | 0.001133165 | 0.037746444 |
| 3 | MYB7 | 6 | 0.8379205 | 0.45259939 | 0.38532110 | 0.947468153 | 0.0007034644 | 0.13880090 | 0.00000000 | 0.000000000 | ... | 0.000000000 | 0.000000000 | 0.0000000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 |
| 4 | KNAT7 | 6 | 0.3822630 | 0.33639144 | 0.04587156 | 0.289581809 | 0.0006074321 | 0.09458828 | 0.01126761 | 0.005633803 | ... | 0.000000000 | 0.000000000 | 0.0000000000 | 0.00000000 | 0.71904762 | 0.284126984 | 0.434920635 | 0.852792288 | 0.001607269 | 0.122205425 |
| 5 | ATHB13 | 6 | 0.3363914 | 0.03058104 | 0.30581040 | 0.018348624 | 0.0007202355 | 0.08535107 | 0.06760563 | 0.011267606 | ... | 0.000000000 | 0.000000000 | 0.0000000000 | 0.00000000 | 0.01904762 | 0.003174603 | 0.015873016 | 0.056128397 | 0.001144112 | 0.010720118 |
| 6 | RAV1 | 6 | 0.4006116 | 0.22935780 | 0.17125382 | 0.004493349 | 0.0005668635 | 0.10055625 | 0.10704225 | 0.084507042 | ... | 0.056497175 | 0.952626679 | 0.0002896641 | 0.05385743 | 0.14285714 | 0.092063492 | 0.050793651 | 0.450818886 | 0.001049787 | 0.047376558 |
In [15]:
head(el2mat)
| X | tf_occurance | atri_degree_centrality | atri_out_centrality | atri_in_centrality | atri_betweenness_centrality | atri_closeness_centrality | atri_eigenvector_centrality | tri_degree_centrality | tri_out_centrality | ... | phl_in_centrality | phl_betweenness_centrality | phl_closeness_centrality | phl_eigenvector_centrality | col_degree_centrality | col_out_centrality | col_in_centrality | col_betweenness_centrality | col_closeness_centrality | col_eigenvector_centrality | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ... | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | HB-1 | 8 | 0.2195122 | 0.1324042 | 0.08710801 | 0.0002802076 | 0.0004585878 | 0.06551537 | 0.10238908 | 0.027303754 | ... | 0.02790015 | 0.000000e+00 | 0.0003329542 | 0.04644030 | 0.41637631 | 0.109756098 | 0.30662021 | 0.3944822470 | 0.0015520636 | 0.09681453 |
| 2 | WLIM1 | 7 | 0.5296167 | 0.2891986 | 0.24041812 | 0.9633171706 | 0.0005781904 | 0.10994336 | 0.10238908 | 0.088737201 | ... | 0.03817915 | 1.390688e-03 | 0.0004631928 | 0.05127801 | 0.00000000 | 0.000000000 | 0.00000000 | 0.0000000000 | 0.0000000000 | 0.00000000 |
| 3 | RD26 | 7 | 0.7386760 | 0.5017422 | 0.23693380 | 0.9363441437 | 0.0006638061 | 0.13094847 | 0.27303754 | 0.187713311 | ... | 0.04405286 | 1.943509e-05 | 0.0003849459 | 0.04142436 | 0.01916376 | 0.006968641 | 0.01219512 | 0.0000000000 | 0.0009426614 | 0.01042103 |
| 4 | MYB7 | 7 | 0.6236934 | 0.4250871 | 0.19860627 | 0.9121244609 | 0.0006516830 | 0.11882806 | 0.02730375 | 0.003412969 | ... | 0.00000000 | 0.000000e+00 | 0.0000000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.0000000000 | 0.0000000000 | 0.00000000 |
| 5 | KNAT7 | 7 | 0.5783972 | 0.3972125 | 0.18118467 | 0.2351063571 | 0.0006330818 | 0.11903518 | 0.00000000 | 0.000000000 | ... | 0.00000000 | 0.000000e+00 | 0.0000000000 | 0.00000000 | 0.62020906 | 0.268292683 | 0.35191638 | 0.8681339730 | 0.0013507501 | 0.11408248 |
| 6 | BLH3 | 6 | 0.1428571 | 0.1149826 | 0.02787456 | 0.0151799420 | 0.0004492747 | 0.04630695 | 0.01365188 | 0.010238908 | ... | 0.00000000 | 0.000000e+00 | 0.0000000000 | 0.00000000 | 0.11672474 | 0.085365854 | 0.03135889 | 0.0004317395 | 0.0012679092 | 0.04344304 |
In [16]:
head(trans2el)
| X | tf_occurance | atri_degree_centrality | atri_out_centrality | atri_in_centrality | atri_betweenness_centrality | atri_closeness_centrality | atri_eigenvector_centrality | tri_degree_centrality | tri_out_centrality | ... | phl_in_centrality | phl_betweenness_centrality | phl_closeness_centrality | phl_eigenvector_centrality | col_degree_centrality | col_out_centrality | col_in_centrality | col_betweenness_centrality | col_closeness_centrality | col_eigenvector_centrality | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ... | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | AT4G30410 | 6 | 0.7468354 | 0.60506329 | 0.1417722 | 0.856499390 | 0.0004675479 | 0.15472441 | 0.8629550 | 0.53319058 | ... | 0.00000000 | 0.0000000 | 0.0000000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.0000000 | 0.0000000000 | 0.00000000 |
| 2 | ATCTH | 6 | 0.3088608 | 0.05569620 | 0.2531646 | 0.002081861 | 0.0004639463 | 0.10061684 | 0.1605996 | 0.02569593 | ... | 0.00000000 | 0.0000000 | 0.0000000000 | 0.00000000 | 0.35532995 | 0.299492386 | 0.05583756 | 0.7610230 | 0.0010902563 | 0.08569533 |
| 3 | IAA14 | 5 | 0.3392405 | 0.25569620 | 0.0835443 | 0.009760329 | 0.0003937151 | 0.10527274 | 0.2312634 | 0.13918630 | ... | 0.00000000 | 0.0000000 | 0.0000000000 | 0.00000000 | 0.01649746 | 0.001269036 | 0.01522843 | 0.0000000 | 0.0007651091 | 0.01138349 |
| 4 | BIM1 | 5 | 0.5316456 | 0.31645570 | 0.2151899 | 0.011379554 | 0.0004630263 | 0.13175744 | 0.1070664 | 0.06209850 | ... | 0.07387141 | 0.0000000 | 0.0003685896 | 0.04403974 | 0.27918782 | 0.189086294 | 0.09010152 | 0.9445656 | 0.0012919391 | 0.07964298 |
| 5 | GATA4 | 5 | 0.2911392 | 0.04556962 | 0.2455696 | 0.001702757 | 0.0005350022 | 0.09326911 | 0.6295503 | 0.02783726 | ... | 0.00000000 | 0.0000000 | 0.0000000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.0000000 | 0.0000000000 | 0.00000000 |
| 6 | HAT2 | 5 | 0.0000000 | 0.00000000 | 0.0000000 | 0.000000000 | 0.0000000000 | 0.00000000 | 0.0000000 | 0.00000000 | ... | 0.07250342 | 0.9518112 | 0.0003552297 | 0.05053935 | 0.00000000 | 0.000000000 | 0.00000000 | 0.0000000 | 0.0000000000 | 0.00000000 |
In [17]:
min_max_normalize <- function(data) {
min_val <- min(data)
max_val <- max(data)
normalized_data <- (data - min_val) / (max_val - min_val)
return(normalized_data)
}
In [18]:
summary(stem2pro$tri_betweenness_centrality)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00000 0.00000 0.00000 0.03773 0.00000 0.98370
In [19]:
summary(min_max_normalize(stem2pro$tri_betweenness_centrality))
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00000 0.00000 0.00000 0.03835 0.00000 1.00000
In [20]:
ncol(stem2pro)
62
In [21]:
stem2pro <- as.data.frame(cbind(stem2pro[,1],apply(stem2pro[,3:62],2,min_max_normalize)))
pro2trans <- as.data.frame(cbind(pro2trans[,1],apply(pro2trans[,3:62],2,min_max_normalize)))
trans2el <- as.data.frame(cbind(trans2el[,1],apply(trans2el[,3:62],2,min_max_normalize)))
el2el <- as.data.frame(cbind(el2el[,1],apply(el2el[,3:62],2,min_max_normalize)))
el2mat <- as.data.frame(cbind(el2mat[,1],apply(el2mat[,3:62],2,min_max_normalize)))
In [22]:
dat <- stem2pro %>%
full_join(pro2trans, by = "V1") %>%
full_join(trans2el, by = "V1") %>%
full_join(el2el, by = "V1") %>%
full_join(el2mat, by = "V1")
In [23]:
dat[is.na(dat)] <- 0
In [24]:
n <- c('atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality',
'tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality',
'lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality',
'cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality',
'end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality',
'per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality',
'pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality',
'xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality',
'phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality',
'col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality')
In [25]:
colnames(dat) <- c("TF",gsub("$","_1",n), gsub("$","_2",n),gsub("$","_3",n),gsub("$","_4",n),gsub("$","_5",n))
In [26]:
GeneID <- wanted_TFs$GeneID[match(dat$TF, wanted_TFs$Name)]
In [27]:
dat <- cbind(GeneID, dat)
In [28]:
head(dat)
| GeneID | TF | atri_degree_centrality_1 | atri_out_centrality_1 | atri_in_centrality_1 | atri_betweenness_centrality_1 | atri_closeness_centrality_1 | atri_eigenvector_centrality_1 | tri_degree_centrality_1 | tri_out_centrality_1 | ... | phl_in_centrality_5 | phl_betweenness_centrality_5 | phl_closeness_centrality_5 | phl_eigenvector_centrality_5 | col_degree_centrality_5 | col_out_centrality_5 | col_in_centrality_5 | col_betweenness_centrality_5 | col_closeness_centrality_5 | col_eigenvector_centrality_5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | ... | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | |
| 1 | AT1G08880 | H2AXA | 0.280558789289872 | 0.276995305164319 | 0.237911025145068 | 0.040883332588188 | 0.77982289908458 | 0.638016012368832 | 0.286472148541114 | 0.147186147186147 | ... | 0.0161527165932452 | 0 | 0.5331869128049 | 0.0917789928495729 | 0.0385462555066079 | 0.0127931769722815 | 0.061965811965812 | 0.00594437414013402 | 0.644609523078635 | 0.182176552700344 |
| 2 | AT4G16780 | HB-2 | 0.712456344586729 | 0.408450704225352 | 0.847195357833656 | 0.88525785755801 | 0.975785470219646 | 0.949927136438232 | 0.246684350132626 | 0.0432900432900433 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | AT4G36620 | GATA19 | 0.711292200232829 | 0.889671361502347 | 0.448742746615087 | 0.990133343765369 | 0.949610304448685 | 0.934827955774441 | 0.214854111405836 | 0.142857142857143 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | AT1G51060 | HTA10 | 0.320139697322468 | 0.314553990610329 | 0.272727272727273 | 0.564467183797559 | 0.798064297939221 | 0.676041679289166 | 0.326259946949602 | 0.177489177489178 | ... | 0.0220264317180617 | 0 | 0.568027911267958 | 0.120142431316983 | 0.00770925110132159 | 0.00213219616204691 | 0.0128205128205128 | 0 | 0.483551661993331 | 0.0541577050018262 |
| 5 | AT1G54690 | GAMMA-H2AX | 0.409778812572759 | 0.528169014084507 | 0.245647969052224 | 0.871230495819734 | 0.826823493068053 | 0.732753574021271 | 0.70026525198939 | 0.692640692640693 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | AT2G21060 | GRP2B | 0.237485448195576 | 0.267605633802817 | 0.174081237911025 | 0.904043903965664 | 0.808125480825825 | 0.578030536398732 | 0 | 0 | ... | 0.110132158590308 | 0.00384910102502027 | 0.807871207844227 | 0.612066867831852 | 0.00991189427312775 | 0.00426439232409382 | 0.014957264957265 | 0 | 0.459717836582851 | 0.0640027480741151 |
In [29]:
numz <- function(x){
sum(x==0)/length(x)
}
In [30]:
dat$combined_score <- min_max_normalize(rowSums(apply(dat[,grep("centrality",colnames(dat))],2,as.numeric)))
dat$celltype_specificity <- min_max_normalize(apply(apply(dat[,grep("centrality",colnames(dat))],2,as.numeric),1,numz))
dat$weighted_score <- dat$combined_score + dat$celltype_specificity
dat <- dat %>% arrange(desc(weighted_score))
In [31]:
head(dat)
| GeneID | TF | atri_degree_centrality_1 | atri_out_centrality_1 | atri_in_centrality_1 | atri_betweenness_centrality_1 | atri_closeness_centrality_1 | atri_eigenvector_centrality_1 | tri_degree_centrality_1 | tri_out_centrality_1 | ... | phl_eigenvector_centrality_5 | col_degree_centrality_5 | col_out_centrality_5 | col_in_centrality_5 | col_betweenness_centrality_5 | col_closeness_centrality_5 | col_eigenvector_centrality_5 | combined_score | celltype_specificity | weighted_score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | ... | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> | |
| 1 | AT5G24800 | BZIP9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.965592719930867 | 0 | 0 | 0 | 0 | 0 | 0 | 0.7720647 | 0.5822222 | 1.354287 |
| 2 | AT3G43430 | AT3G43430 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.390917063818791 | 0 | 0 | 0 | 0 | 0 | 0 | 0.9221986 | 0.4222222 | 1.344421 |
| 3 | AT2G45050 | GATA2 | 0.828870779976717 | 0.995305164319249 | 0.55705996131528 | 0.578715294854026 | 0.984309713319463 | 0.979686642330802 | 0.838196286472148 | 0.961038961038961 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.8763536 | 0.4311111 | 1.307465 |
| 4 | AT5G13910 | LEP | 0 | 0 | 0 | 0 | 0 | 0 | 0.10079575596817 | 0.155844155844156 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.0000000 | 0.3066667 | 1.306667 |
| 5 | AT1G66230 | MYB20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0606489225228414 | 0 | 0 | 0 | 0 | 0 | 0 | 0.7950683 | 0.4844444 | 1.279513 |
| 6 | AT1G07640 | OBP2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.982494065594694 | 0 | 0 | 0 | 0 | 0 | 0 | 0.5664295 | 0.6800000 | 1.246430 |
In [32]:
write.csv(dat,"TF_GRN_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=FALSE)
In [33]:
dat_genesys <- dat
Load GRN centrality scores for each transition (Linear Interaction Matrix, LIMA) (scRNAseq)¶
In [34]:
stem2pro <- read.csv("./scRNAseq_TF_GRN_centrality_t0-t1_zscore3.csv")
pro2trans <- read.csv("./scRNAseq_TF_GRN_centrality_t1-t3_zscore3.csv")
trans2el <- read.csv("./scRNAseq_TF_GRN_centrality_t3-t5_zscore3.csv")
el2el <- read.csv("./scRNAseq_TF_GRN_centrality_t5-t7_zscore3.csv")
el2mat <- read.csv("./scRNAseq_TF_GRN_centrality_t7-t9_zscore3.csv")
In [35]:
head(stem2pro)
| X | tf_occurance | atri_degree_centrality | atri_out_centrality | atri_in_centrality | atri_betweenness_centrality | atri_closeness_centrality | atri_eigenvector_centrality | tri_degree_centrality | tri_out_centrality | ... | phl_in_centrality | phl_betweenness_centrality | phl_closeness_centrality | phl_eigenvector_centrality | col_degree_centrality | col_out_centrality | col_in_centrality | col_betweenness_centrality | col_closeness_centrality | col_eigenvector_centrality | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ... | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | BBX29 | 10 | 0.2473118 | 0.05376344 | 0.19354839 | 0.0024418167 | 0.0002779949 | 0.07634188 | 0.11006289 | 0.006289308 | ... | 0.08914100 | 0.0360194910 | 1.067095e-04 | 0.12047289 | 0.4209184 | 0.1403061 | 0.28061224 | 4.642662e-04 | 0.0002109170 | 0.06890260 |
| 2 | ATWHY2 | 10 | 0.3252688 | 0.29032258 | 0.03494624 | 0.0008767354 | 0.0003126635 | 0.08784270 | 0.10377358 | 0.081761006 | ... | 0.03079417 | 0.0002762634 | 8.076810e-05 | 0.04041359 | 0.1275510 | 0.0752551 | 0.05229592 | 3.810241e-03 | 0.0001721267 | 0.03419012 |
| 3 | HAT1 | 10 | 0.2795699 | 0.11290323 | 0.16666667 | 0.0092383271 | 0.0002821786 | 0.07954667 | 0.07861635 | 0.003144654 | ... | 0.12965964 | 0.0188043318 | 1.094541e-04 | 0.13846171 | 0.3558673 | 0.1160714 | 0.23979592 | 2.687857e-04 | 0.0002097424 | 0.06503697 |
| 4 | HMGB5 | 10 | 0.7204301 | 0.52150538 | 0.19892473 | 0.0658058720 | 0.0002974142 | 0.13050351 | 0.34591195 | 0.248427673 | ... | 0.11669368 | 0.0003288851 | 1.050922e-04 | 0.06751047 | 0.4311224 | 0.1568878 | 0.27423469 | 3.502359e-04 | 0.0002140065 | 0.07247327 |
| 5 | AT4G25210 | 10 | 0.1344086 | 0.03763441 | 0.09677419 | 0.0001956352 | 0.0002611916 | 0.05197195 | 0.04402516 | 0.009433962 | ... | 0.04862237 | 0.0003183607 | 8.684771e-05 | 0.05319400 | 0.1441327 | 0.0255102 | 0.11862245 | 6.516016e-06 | 0.0001854091 | 0.03889094 |
| 6 | HB-2 | 10 | 0.5645161 | 0.09408602 | 0.47043011 | 0.0330406052 | 0.0003468205 | 0.11220601 | 0.37735849 | 0.015723270 | ... | 0.35818476 | 0.2002541624 | 1.248236e-04 | 0.15678872 | 1.1607143 | 0.4107143 | 0.75000000 | 7.682383e-03 | 0.0002419695 | 0.10843956 |
In [36]:
head(pro2trans)
| X | tf_occurance | atri_degree_centrality | atri_out_centrality | atri_in_centrality | atri_betweenness_centrality | atri_closeness_centrality | atri_eigenvector_centrality | tri_degree_centrality | tri_out_centrality | ... | phl_in_centrality | phl_betweenness_centrality | phl_closeness_centrality | phl_eigenvector_centrality | col_degree_centrality | col_out_centrality | col_in_centrality | col_betweenness_centrality | col_closeness_centrality | col_eigenvector_centrality | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ... | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | HMGA | 8 | 0.23185484 | 0.13709677 | 0.09475806 | 0.015668785 | 3.412046e-05 | 0.08995626 | 0.05423729 | 0.010169492 | ... | 0.36858316 | 1.410781e-03 | 0.0004441978 | 0.09035502 | 0.03061224 | 0.01473923 | 0.015873016 | 0.0000000000 | 0.0001264495 | 0.01336338 |
| 2 | HTA2 | 8 | 0.20766129 | 0.03427419 | 0.17338710 | 0.008207071 | 3.612809e-05 | 0.08060176 | 0.04745763 | 0.005084746 | ... | 0.14784394 | 6.074694e-03 | 0.0003969372 | 0.04766510 | 0.05668934 | 0.04875283 | 0.007936508 | 0.0000000000 | 0.0001197137 | 0.02249220 |
| 3 | H2AXA | 8 | 0.29435484 | 0.17741935 | 0.11693548 | 0.859184588 | 3.461035e-05 | 0.09814893 | 0.11525424 | 0.020338983 | ... | 0.10472279 | 0.000000e+00 | 0.0003790451 | 0.03986205 | 0.12698413 | 0.08956916 | 0.037414966 | 0.0006421789 | 0.0001507530 | 0.04020922 |
| 4 | HAT1 | 7 | 0.07056452 | 0.02016129 | 0.05040323 | 0.001026393 | 2.769404e-05 | 0.03625940 | 0.01016949 | 0.001694915 | ... | 0.10061602 | 2.479682e-04 | 0.0003653150 | 0.04506419 | 0.00000000 | 0.00000000 | 0.000000000 | 0.0000000000 | 0.0000000000 | 0.00000000 |
| 5 | AT4G30410 | 7 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.000000e+00 | 0.00000000 | 0.32033898 | 0.279661017 | ... | 0.04517454 | 2.110368e-06 | 0.0003014758 | 0.02996103 | 0.00000000 | 0.00000000 | 0.000000000 | 0.0000000000 | 0.0000000000 | 0.00000000 |
| 6 | GATA15 | 7 | 0.12096774 | 0.08467742 | 0.03629032 | 0.020193874 | 2.927206e-05 | 0.05440149 | 0.12881356 | 0.018644068 | ... | 0.09958932 | 0.000000e+00 | 0.0003734896 | 0.03880090 | 0.00000000 | 0.00000000 | 0.000000000 | 0.0000000000 | 0.0000000000 | 0.00000000 |
In [37]:
head(trans2el)
| X | tf_occurance | atri_degree_centrality | atri_out_centrality | atri_in_centrality | atri_betweenness_centrality | atri_closeness_centrality | atri_eigenvector_centrality | tri_degree_centrality | tri_out_centrality | ... | phl_in_centrality | phl_betweenness_centrality | phl_closeness_centrality | phl_eigenvector_centrality | col_degree_centrality | col_out_centrality | col_in_centrality | col_betweenness_centrality | col_closeness_centrality | col_eigenvector_centrality | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ... | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | UPB1 | 7 | 0.53893130 | 0.512977099 | 0.02595420 | 0.033242291 | 0.0002219147 | 0.16024940 | 0.16666667 | 0.151162791 | ... | 0.09872923 | 0.011076933 | 0.0003917588 | 0.055477052 | 0.005228758 | 0.00130719 | 0.003921569 | 0.000000e+00 | 0.0001937717 | 0.003017745 |
| 2 | ARF9 | 7 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.0000000000 | 0.00000000 | 0.00000000 | 0.000000000 | ... | 0.03225806 | 0.100595310 | 0.0002898337 | 0.026006215 | 0.053594771 | 0.00130719 | 0.052287582 | 1.710981e-06 | 0.0003180017 | 0.017173520 |
| 3 | BZIP61 | 7 | 0.10534351 | 0.024427481 | 0.08091603 | 0.001449681 | 0.0001888281 | 0.06969093 | 0.09496124 | 0.003875969 | ... | 0.11143695 | 0.003736947 | 0.0003968568 | 0.049083955 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000e+00 | 0.0000000000 | 0.000000000 |
| 4 | AT4G30410 | 6 | 0.45343511 | 0.432061069 | 0.02137405 | 0.018397647 | 0.0002189595 | 0.14230160 | 0.59883721 | 0.472868217 | ... | 0.04594330 | 0.000000000 | 0.0003403894 | 0.030540332 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000e+00 | 0.0000000000 | 0.000000000 |
| 5 | HAT22 | 6 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.0000000000 | 0.00000000 | 0.00000000 | 0.000000000 | ... | 0.05083089 | 0.506690540 | 0.0002882157 | 0.028701584 | 0.103267974 | 0.07973856 | 0.023529412 | 3.079766e-05 | 0.0002574170 | 0.030739013 |
| 6 | H2AXA | 6 | 0.05801527 | 0.004580153 | 0.05343511 | 0.000000000 | 0.0001480042 | 0.04784942 | 0.09689922 | 0.005813953 | ... | 0.01368524 | 0.000000000 | 0.0002313431 | 0.009972983 | 0.092810458 | 0.04313725 | 0.049673203 | 1.356808e-03 | 0.0002884427 | 0.028117518 |
In [38]:
head(el2el)
| X | tf_occurance | atri_degree_centrality | atri_out_centrality | atri_in_centrality | atri_betweenness_centrality | atri_closeness_centrality | atri_eigenvector_centrality | tri_degree_centrality | tri_out_centrality | ... | phl_in_centrality | phl_betweenness_centrality | phl_closeness_centrality | phl_eigenvector_centrality | col_degree_centrality | col_out_centrality | col_in_centrality | col_betweenness_centrality | col_closeness_centrality | col_eigenvector_centrality | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ... | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | RD26 | 8 | 0.42589118 | 0.36585366 | 0.060037523 | 1.403638e-01 | 0.0002946191 | 0.11814561 | 0.13519814 | 0.09324009 | ... | 0.05543237 | 2.830066e-05 | 0.0003252977 | 0.05144567 | 0.01660281 | 0.01149425 | 0.005108557 | 3.592975e-05 | 0.0002165590 | 0.008456652 |
| 2 | IAA28 | 8 | 0.10318949 | 0.08442777 | 0.018761726 | 2.257050e-03 | 0.0002285364 | 0.04622373 | 0.02331002 | 0.01165501 | ... | 0.14523282 | 5.191325e-03 | 0.0004077202 | 0.08330795 | 0.00000000 | 0.00000000 | 0.000000000 | 0.000000e+00 | 0.0000000000 | 0.000000000 |
| 3 | RAV1 | 7 | 0.15572233 | 0.14071295 | 0.015009381 | 0.000000e+00 | 0.0002342852 | 0.06301304 | 0.06759907 | 0.03729604 | ... | 0.04323725 | 6.152317e-06 | 0.0003384714 | 0.03899602 | 0.17241379 | 0.08556833 | 0.086845466 | 9.390729e-04 | 0.0003018161 | 0.050018984 |
| 4 | AT5G51780 | 7 | 0.03752345 | 0.03564728 | 0.001876173 | 3.392628e-03 | 0.0002174286 | 0.01919495 | 0.03496503 | 0.02331002 | ... | 0.07427938 | 5.336519e-03 | 0.0003468874 | 0.04246925 | 0.00000000 | 0.00000000 | 0.000000000 | 0.000000e+00 | 0.0000000000 | 0.000000000 |
| 5 | ARR6 | 7 | 0.52345216 | 0.49906191 | 0.024390244 | 6.424128e-01 | 0.0003168322 | 0.13466434 | 0.15384615 | 0.11888112 | ... | 0.05764967 | 3.210279e-03 | 0.0003430130 | 0.03354806 | 0.03320562 | 0.02426564 | 0.008939974 | 4.605540e-04 | 0.0001841784 | 0.014827200 |
| 6 | HAT22 | 7 | 0.09380863 | 0.06941839 | 0.024390244 | 3.526640e-06 | 0.0002211932 | 0.04450810 | 0.37529138 | 0.34965035 | ... | 0.00443459 | 4.499115e-01 | 0.0002499909 | 0.01713840 | 0.15581098 | 0.05491699 | 0.100893997 | 1.469853e-04 | 0.0003284582 | 0.049621261 |
In [39]:
head(el2mat)
| X | tf_occurance | atri_degree_centrality | atri_out_centrality | atri_in_centrality | atri_betweenness_centrality | atri_closeness_centrality | atri_eigenvector_centrality | tri_degree_centrality | tri_out_centrality | ... | phl_in_centrality | phl_betweenness_centrality | phl_closeness_centrality | phl_eigenvector_centrality | col_degree_centrality | col_out_centrality | col_in_centrality | col_betweenness_centrality | col_closeness_centrality | col_eigenvector_centrality | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ... | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | AT5G57150 | 7 | 0.07066381 | 0.02355460 | 0.04710921 | 1.838049e-05 | 0.0002271020 | 0.04257109 | 0.00000000 | 0.000000000 | ... | 0.05637255 | 0.0009833995 | 0.0003705820 | 0.04461017 | 0.01322751 | 0.007936508 | 0.005291005 | 5.781562e-05 | 0.0001494290 | 0.00619152 |
| 2 | HB-1 | 7 | 0.11777302 | 0.07066381 | 0.04710921 | 9.236199e-04 | 0.0001976504 | 0.05363112 | 0.06060606 | 0.035984848 | ... | 0.05392157 | 0.0008270179 | 0.0003624831 | 0.04837457 | 0.22883598 | 0.099206349 | 0.129629630 | 8.269386e-04 | 0.0003296181 | 0.06256898 |
| 3 | KNAT7 | 7 | 0.48179872 | 0.23768737 | 0.24411135 | 3.569354e-01 | 0.0002732558 | 0.12798488 | 0.01704545 | 0.009469697 | ... | 0.00000000 | 0.0000000000 | 0.0000000000 | 0.00000000 | 0.22883598 | 0.126984127 | 0.101851852 | 1.228144e-03 | 0.0002905428 | 0.06016443 |
| 4 | WLIM1 | 7 | 0.15417559 | 0.10920771 | 0.04496788 | 5.652002e-03 | 0.0002372044 | 0.06107945 | 0.10037879 | 0.066287879 | ... | 0.06372549 | 0.0021382173 | 0.0003827076 | 0.05274886 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000e+00 | 0.0000000000 | 0.00000000 |
| 5 | HAT22 | 7 | 0.09207709 | 0.06852248 | 0.02355460 | 2.109162e-03 | 0.0002321057 | 0.04775775 | 0.52272727 | 0.346590909 | ... | 0.01102941 | 0.0000000000 | 0.0002210693 | 0.01437458 | 0.20105820 | 0.091269841 | 0.109788360 | 1.245664e-03 | 0.0003292806 | 0.05612659 |
| 6 | RD26 | 7 | 0.54389722 | 0.34261242 | 0.20128480 | 2.060913e-01 | 0.0002902954 | 0.13766910 | 0.30871212 | 0.164772727 | ... | 0.01960784 | 0.0248150487 | 0.0002323900 | 0.02779856 | 0.02910053 | 0.010582011 | 0.018518519 | 4.029574e-05 | 0.0001804397 | 0.01076868 |
In [40]:
stem2pro <- as.data.frame(cbind(stem2pro[,1],apply(stem2pro[,3:62],2,min_max_normalize)))
pro2trans <- as.data.frame(cbind(pro2trans[,1],apply(pro2trans[,3:62],2,min_max_normalize)))
trans2el <- as.data.frame(cbind(trans2el[,1],apply(trans2el[,3:62],2,min_max_normalize)))
el2el <- as.data.frame(cbind(el2el[,1],apply(el2el[,3:62],2,min_max_normalize)))
el2mat <- as.data.frame(cbind(el2mat[,1],apply(el2mat[,3:62],2,min_max_normalize)))
In [41]:
dat <- stem2pro %>%
full_join(pro2trans, by = "V1") %>%
full_join(trans2el, by = "V1") %>%
full_join(el2el, by = "V1") %>%
full_join(el2mat, by = "V1")
In [42]:
dat[is.na(dat)] <- 0
In [43]:
n <- c('atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality',
'tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality',
'lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality',
'cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality',
'end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality',
'per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality',
'pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality',
'xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality',
'phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality',
'col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality')
In [44]:
colnames(dat) <- c("TF",gsub("$","_1",n), gsub("$","_2",n),gsub("$","_3",n),gsub("$","_4",n),gsub("$","_5",n))
In [45]:
GeneID <- wanted_TFs$GeneID[match(dat$TF, wanted_TFs$Name)]
In [46]:
dat <- cbind(GeneID, dat)
In [47]:
head(dat)
| GeneID | TF | atri_degree_centrality_1 | atri_out_centrality_1 | atri_in_centrality_1 | atri_betweenness_centrality_1 | atri_closeness_centrality_1 | atri_eigenvector_centrality_1 | tri_degree_centrality_1 | tri_out_centrality_1 | ... | phl_in_centrality_5 | phl_betweenness_centrality_5 | phl_closeness_centrality_5 | phl_eigenvector_centrality_5 | col_degree_centrality_5 | col_out_centrality_5 | col_in_centrality_5 | col_betweenness_centrality_5 | col_closeness_centrality_5 | col_eigenvector_centrality_5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | ... | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | |
| 1 | AT5G54470 | BBX29 | 0.211009174311927 | 0.0564971751412429 | 0.194070080862534 | 0.00792493650644342 | 0.680594361071223 | 0.496028162328826 | 0.114754098360656 | 0.00677966101694915 | ... | 0.0028735632183908 | 0.189308951096575 | 0.458539996496892 | 0.0482709631770397 | 0.00621976503109883 | 0.00542740841248304 | 0.00704225352112676 | 0 | 0.386781235987834 | 0.0414438766680971 |
| 2 | AT1G71260 | ATWHY2 | 0.277522935779817 | 0.305084745762712 | 0.0350404312668464 | 0.00284545198005832 | 0.765470980339886 | 0.57075423509666 | 0.108196721311475 | 0.088135593220339 | ... | 0.0244252873563218 | 4.58083105436988e-06 | 0.594686840322569 | 0.140802047799801 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | AT4G17460 | HAT1 | 0.238532110091743 | 0.11864406779661 | 0.16711590296496 | 0.0299830683849121 | 0.69083699073779 | 0.516851164189586 | 0.0819672131147541 | 0.00338983050847458 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | AT4G35570 | HMGB5 | 0.614678899082569 | 0.548022598870057 | 0.199460916442049 | 0.21357351142884 | 0.728137096243869 | 0.847941081443942 | 0.360655737704918 | 0.267796610169492 | ... | 0.0387931034482759 | 0 | 0.67922660278798 | 0.146857216289362 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | AT4G25210 | AT4G25210 | 0.114678899082569 | 0.0395480225988701 | 0.0970350404312668 | 0.000634935565798138 | 0.639456025426685 | 0.337685533756012 | 0.0459016393442623 | 0.0101694915254237 | ... | 0.0459770114942529 | 3.05388736957992e-06 | 0.688056927350787 | 0.233155293081824 | 0.0055286800276434 | 0.00135685210312076 | 0.00985915492957747 | 0 | 0.427169557140715 | 0.0415273011938073 |
| 6 | AT4G16780 | HB-2 | 0.481651376146789 | 0.0988700564971751 | 0.471698113207547 | 0.107233562223685 | 0.849095044620969 | 0.72905381296083 | 0.39344262295082 | 0.0169491525423729 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
In [48]:
dat$combined_score <- min_max_normalize(rowSums(apply(dat[,grep("centrality",colnames(dat))],2,as.numeric)))
dat$celltype_specificity <- min_max_normalize(apply(apply(dat[,grep("centrality",colnames(dat))],2,as.numeric),1,numz))
dat$weighted_score <- dat$combined_score + dat$celltype_specificity
dat <- dat %>% arrange(desc(weighted_score))
In [49]:
head(dat)
| GeneID | TF | atri_degree_centrality_1 | atri_out_centrality_1 | atri_in_centrality_1 | atri_betweenness_centrality_1 | atri_closeness_centrality_1 | atri_eigenvector_centrality_1 | tri_degree_centrality_1 | tri_out_centrality_1 | ... | phl_eigenvector_centrality_5 | col_degree_centrality_5 | col_out_centrality_5 | col_in_centrality_5 | col_betweenness_centrality_5 | col_closeness_centrality_5 | col_eigenvector_centrality_5 | combined_score | celltype_specificity | weighted_score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | ... | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> | |
| 1 | AT2G45050 | GATA2 | 0.740825688073395 | 0.836158192090396 | 0.0727762803234501 | 0.164683472862384 | 0.898810177742123 | 0.923795434525079 | 1 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.9218566 | 0.5850622 | 1.506919 |
| 2 | AT5G24800 | BZIP9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.893177964282472 | 0 | 0 | 0 | 0 | 0 | 0 | 0.7714303 | 0.7012448 | 1.472675 |
| 3 | AT3G43430 | AT3G43430 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.476672700564731 | 0 | 0 | 0 | 0 | 0 | 0 | 0.9013202 | 0.5394191 | 1.440739 |
| 4 | AT5G13910 | LEP | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0248671080637247 | 0 | 0 | 0 | 0 | 0 | 0 | 0.7905920 | 0.6058091 | 1.396401 |
| 5 | AT1G75710 | BRON | 0.809633027522936 | 0.42090395480226 | 0.549865229110512 | 0.305121813564105 | 0.852808377917617 | 0.903605582412613 | 0.380327868852459 | 0.00677966101694915 | ... | 0 | 0.0400829302004147 | 0.0067842605156038 | 0.0746478873239437 | 0.000524813035356154 | 0.739388969226133 | 0.236949389676998 | 0.7574624 | 0.6348548 | 1.392317 |
| 6 | AT4G32880 | HB-8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.146825077321862 | 0 | 0 | 0 | 0 | 0 | 0 | 0.7588265 | 0.6265560 | 1.385383 |
In [50]:
write.csv(dat,"scRNAseq_TF_GRN_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=FALSE)
In [51]:
dat_raw <- dat
GO terms (Prepare GS)¶
In [52]:
gene_list <- read.table('./gene_list_1108.csv', sep=",", header = TRUE)
In [53]:
exptf <- intersect(gene_list$features, wanted_TFs$GeneID)
length(exptf)
1495
In [54]:
sfun <- read.csv('string_functional_annotations.tsv', sep="\t", header=TRUE)
sann <- read.csv('string_protein_annotations.tsv', sep="\t", header=TRUE)
In [55]:
gsgo1 <- unique(sfun[grep('root|xylem|phloem|procambium|pericycle|vascular|vasculature|stele|tracheary|sieve|trichoblast|atrichoblast|epidermis|epidermal tissue|lateral root cap|root hair|trichome|cortex|endodermis|ground tissue|columella|quiescent center',sfun$term.description, ignore.case=TRUE),]$X.node)
gsgo2 <- unique(sann[grep('root|xylem|phloem|procambium|pericycle|vascular|vasculature|stele|tracheary|sieve|trichoblast|atrichoblast|epidermis|epidermal tissue|lateral root cap|root hair|trichome|cortex|endodermis|ground tissue|columella|quiescent center',sann$domain_summary_url, ignore.case=TRUE),]$X.node)
gsgo <- sort(unique(c(gsgo1, gsgo2)))
In [56]:
#write.csv(data.frame(GeneID=gsgo),"./Gold_Standard_Root_TF_StringDB.csv", quote=FALSE, row.names=FALSE)
In [56]:
length(gsgo)
gsgo
209
- 'A0A1I9LNX5'
- 'AGL12'
- 'AGL16'
- 'AHK2'
- 'AHK3'
- 'AHK4'
- 'AIL5'
- 'AL6'
- 'ANL2'
- 'APL'
- 'ARF10'
- 'ARF16'
- 'ARF17'
- 'ARF19'
- 'ARF7'
- 'ARR1'
- 'ARR10'
- 'ARR12'
- 'ARR2'
- 'ATHB-13'
- 'ATHB-15'
- 'ATHB-23'
- 'ATHB-8'
- 'ATHB-X'
- 'ATX1-2'
- 'BEH1'
- 'BHLH12'
- 'BHLH155'
- 'BHLH2'
- 'BHLH32'
- 'BHLH48'
- 'BHLH54'
- 'BHLH66'
- 'BHLH69'
- 'BHLH82'
- 'BHLH83'
- 'BHLH85'
- 'BHLH86'
- 'BIB'
- 'BRM'
- 'BZIP19'
- 'BZIP23'
- 'BZR2'
- 'CHC1-2'
- 'CHR12'
- 'CHR23'
- 'CPC'
- 'CRF2'
- 'DAR2'
- 'DOF1.1'
- 'DOF2.2'
- 'DOF2.4'
- 'DOF3.2'
- 'DOF3.6'
- 'DOF5.1'
- 'DOF5.3'
- 'DOF5.6'
- 'DOT2'
- 'DPB'
- 'E2FB'
- 'EFM'
- 'ERF055'
- 'ERF104'
- 'ET2'
- 'ETC1'
- 'ETR1'
- 'F13K3.12'
- 'F24O1.3'
- 'F27L4.4'
- 'F2I11.230'
- 'FEZ'
- 'GAI'
- 'GALT2'
- 'GATA12'
- 'GATA23'
- 'GIF1'
- 'GIS3'
- 'GL2'
- 'GL3'
- 'GRF2-2'
- 'GRF3-2'
- 'GTL1'
- 'HAG1'
- 'HAT2'
- 'HAT4'
- 'HDG11'
- 'HDG12'
- 'HDG2'
- 'HDT1'
- 'HHO1'
- 'HHO2'
- 'HHO3'
- 'HHO6'
- 'HRS1'
- 'HSFA4C'
- 'IAA12'
- 'IAA14'
- 'IAA20'
- 'IAA28'
- 'IAA3'
- 'IAA31'
- 'IAA8'
- 'IDD4'
- 'IDD6'
- 'IWS1'
- 'JKD'
- 'JMJ14'
- 'JMJ25'
- 'JMJ30'
- 'JMJ32'
- 'K15E6.5'
- 'KAN1'
- 'KNAT7'
- 'KUA1'
- 'LBD15'
- 'LBD16'
- 'LBD18'
- 'LBD29'
- 'LBD3'
- 'LBD30'
- 'LBD4'
- 'LDL1'
- 'LEP'
- 'LHW'
- 'LRP1'
- 'MAMYB'
- 'MGP'
- 'MIF1'
- 'MTERF9'
- 'MYB12'
- 'MYB124'
- 'MYB20'
- 'MYB23'
- 'MYB32'
- 'MYB36'
- 'MYB43'
- 'MYB46'
- 'MYB52'
- 'MYB60'
- 'MYB61'
- 'MYB7'
- 'MYB83'
- 'MYB85'
- 'MYB86'
- 'MYB88'
- 'MYC3'
- 'MYC4'
- 'NAA10'
- 'NAC003'
- 'NAC005'
- 'NAC007'
- 'NAC010'
- 'NAC026'
- 'NAC030'
- 'NAC037'
- 'NAC043'
- 'NAC045'
- 'NAC056'
- 'NAC075'
- 'NAC076'
- 'NAC083'
- 'NAC084'
- 'NAC086'
- 'NAC101'
- 'NAC104'
- 'NAC105'
- 'NAC92'
- 'NUC'
- 'PKL'
- 'PLT1'
- 'PLT2'
- 'PRE3'
- 'Q1G3Q4_ARATH'
- 'RAP2-11'
- 'RAP2-12'
- 'RAV1'
- 'RBR1'
- 'REF6'
- 'REV'
- 'RGA'
- 'SCL22'
- 'SCL27'
- 'SCL6'
- 'SCR'
- 'SCRM'
- 'SHR'
- 'SMB'
- 'SOG1'
- 'T15F16.14'
- 'T2I1.110'
- 'TCP14'
- 'TCP15'
- 'TCP9'
- 'TCX2'
- 'TCX3'
- 'TRY'
- 'TSO1'
- 'TTG1'
- 'UPB1'
- 'WER'
- 'WIP2-2'
- 'WLIM2B'
- 'WOX14'
- 'WRKY13'
- 'WRKY44'
- 'WRKY75'
- 'ZFP5'
- 'ZFP6'
- 'ZFP8'
In [57]:
gsgo <- gsub(",.*$","",gsub("^.*ath:","",sann[match(gsgo, sann$X.node),]$other_names_and_aliases))
gsgo[which(gsgo=='831248')]='AT5G14000'
gsgo
- 'AT3G53680'
- 'AT1G71692'
- 'AT3G57230'
- 'AT5G35750'
- 'AT1G27320'
- 'AT2G01830'
- 'AT5G57390'
- 'AT2G02470'
- 'AT4G00730'
- 'AT1G79430'
- 'AT2G28350'
- 'AT4G30080'
- 'AT1G77850'
- 'AT1G19220'
- 'AT5G20730'
- 'AT3G16857'
- 'AT4G31920'
- 'AT2G25180'
- 'AT4G16110'
- 'AT1G69780'
- 'AT1G52150'
- 'AT1G26960'
- 'AT4G32880'
- 'AT1G70920'
- 'AT2G31650'
- 'AT3G50750'
- 'AT4G00480'
- 'AT2G31280'
- 'AT1G63650'
- 'AT3G25710'
- 'AT2G42300'
- 'AT1G27740'
- 'AT2G24260'
- 'AT4G30980'
- 'AT5G58010'
- 'AT1G66470'
- 'AT4G33880'
- 'AT5G37800'
- 'AT3G45260'
- 'AT2G46020'
- 'AT4G35040'
- 'AT2G16770'
- 'AT1G19350'
- 'AT5G14170'
- 'AT3G06010'
- 'AT5G19310'
- 'AT2G46410'
- 'AT4G23750'
- 'AT2G39830'
- 'AT1G07640'
- 'AT2G28810'
- 'AT2G37590'
- 'AT3G45610'
- 'AT3G55370'
- 'AT5G02460'
- 'AT5G60200'
- 'AT5G62940'
- 'AT5G16780'
- 'AT5G03415'
- 'AT5G22220'
- 'AT2G03500'
- 'AT1G36060'
- 'AT5G61600'
- 'AT5G56780'
- 'AT1G01380'
- 'AT1G66340'
- 'AT2G36720'
- 'AT1G62310'
- 'AT2G23780'
- 'AT5G11340'
- 'AT1G26870'
- 'AT1G14920'
- 'AT4G21060'
- 'AT5G25830'
- 'AT5G26930'
- 'AT5G28640'
- 'AT1G68360'
- 'AT1G79840'
- 'AT5G41315'
- 'AT4G37740'
- 'AT2G36400'
- 'AT1G33240'
- 'AT3G54610'
- 'AT5G47370'
- 'AT4G16780'
- 'AT1G73360'
- 'AT1G17920'
- 'AT1G05230'
- 'AT3G44750'
- 'AT3G25790'
- 'AT1G68670'
- 'AT1G25550'
- 'AT1G49560'
- 'AT1G13300'
- 'AT5G45710'
- 'AT1G04550'
- 'AT4G14550'
- 'AT2G46990'
- 'AT5G25890'
- 'AT1G04240'
- 'AT3G17600'
- 'AT2G22670'
- 'AT2G02080'
- 'AT1G14580'
- 'AT1G32130'
- 'AT5G03150'
- 'AT4G20400'
- 'AT3G07610'
- 'AT3G20810'
- 'AT3G45880'
- 'AT5G38840'
- 'AT5G16560'
- 'AT1G62990'
- 'AT5G47390'
- 'AT2G40470'
- 'AT2G42430'
- 'AT2G45420'
- 'AT3G58190'
- 'AT1G16530'
- 'AT4G00220'
- 'AT1G31320'
- 'AT1G62830'
- 'AT5G13910'
- 'AT2G27230'
- 'AT5G12330'
- 'AT5G45420'
- 'AT1G03840'
- 'AT1G74660'
- 'AT5G55580'
- 'AT2G47460'
- 'AT1G14350'
- 'AT1G66230'
- 'AT5G40330'
- 'AT4G34990'
- 'AT5G57620'
- 'AT5G16600'
- 'AT5G12870'
- 'AT1G17950'
- 'AT1G08810'
- 'AT1G09540'
- 'AT2G16720'
- 'AT3G08500'
- 'AT4G22680'
- 'AT5G26660'
- 'AT2G02820'
- 'AT5G46760'
- 'AT4G17880'
- 'AT5G13780'
- 'AT1G02220'
- 'AT1G02250'
- 'AT1G12260'
- 'AT1G28470'
- 'AT1G62700'
- 'AT1G71930'
- 'AT2G18060'
- 'AT2G46770'
- 'AT3G03200'
- 'AT3G15510'
- 'AT4G29230'
- 'AT4G36160'
- 'AT5G13180'
- 'AT5G14000'
- 'AT5G17260'
- 'AT5G62380'
- 'AT5G64530'
- 'AT5G66300'
- 'AT5G39610'
- 'AT5G44160'
- 'AT2G25170'
- 'AT3G20840'
- 'AT1G51190'
- 'AT1G74500'
- 'AT2G12646'
- 'AT5G19790'
- 'AT1G53910'
- 'AT1G13260'
- 'AT3G12280'
- 'AT3G48430'
- 'AT5G60690'
- 'AT2G01570'
- 'AT3G60630'
- 'AT2G45160'
- 'AT4G00150'
- 'AT3G54220'
- 'AT3G26744'
- 'AT4G37650'
- 'AT1G79580'
- 'AT1G25580'
- 'AT4G08455'
- 'AT5G07400'
- 'AT3G47620'
- 'AT1G69690'
- 'AT2G45680'
- 'AT4G14770'
- 'AT3G22760'
- 'AT5G53200'
- 'AT3G22780'
- 'AT5G24520'
- 'AT2G47270'
- 'AT5G14750'
- 'AT3G57670'
- 'AT3G55770'
- 'AT1G20700'
- 'AT4G39410'
- 'AT2G37260'
- 'AT5G13080'
- 'AT1G10480'
- 'AT1G67030'
- 'AT2G41940'
In [58]:
sann[which(sann$X.node=="HAT7"),]
| X.node | identifier | domain_summary_url | annotation | other_names_and_aliases | |
|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | |
| 668 | HAT7 | 3702.Q00466 | Homeobox-leucine zipper protein HAT7; Probable transcription factor. | https://smart.embl.de/smart/DDvec.cgi?smart=314:HOX(113|174)+ | 831367,AT5G15150,ATHB-3,At5g15150,F8M21_40,HAT7,HAT7_ARATH,HB-3,HD-ZIP protein 7,HD-ZIP protein ATHB-3,Homeobox 3,Homeobox-leucine zipper protein,Homeobox-leucine zipper protein HAT7,Homeodomain transcription factor ATHB-3,Homeodomain-leucine zipper protein HAT7,NM_121519.3,NP_568309,NP_568309.2,Q00466,Q0WNS2,Q9LXG6,ath:AT5G15150 |
In [59]:
sann[grep("831248",sann$other_names_and_aliases, ignore.case = TRUE),]
| X.node | identifier | domain_summary_url | annotation | other_names_and_aliases | |
|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | |
| 980 | NAC084 | 3702.A0A1P8BAC2 | NAC domain containing protein 84. | https://smart.embl.de/smart/DDvec.cgi?smart=262:Pfam_NAM(16|196)+ | 831248,A0A1P8BAC2,A0A1P8BAC2_ARATH,At5g14000,MAC12.3,MAC12_3,NAC domain containing protein 84,NAC084,NM_001343305.1,NP_001330278.1,anac084 |
In [60]:
sfun <- read.csv('./functional_GS_root_unique.csv', sep=",", header=TRUE)
In [61]:
gsgo_miniex <- intersect(exptf, sfun$GeneID)
length(gsgo_miniex)
143
In [62]:
gsgo <- intersect(gsgo, gsgo_miniex)
In [63]:
length(gsgo)
140
In [64]:
gsgo
- 'AT1G71692'
- 'AT5G35750'
- 'AT1G27320'
- 'AT2G01830'
- 'AT5G57390'
- 'AT2G02470'
- 'AT4G00730'
- 'AT1G79430'
- 'AT2G28350'
- 'AT4G30080'
- 'AT1G77850'
- 'AT1G19220'
- 'AT5G20730'
- 'AT3G16857'
- 'AT4G31920'
- 'AT2G25180'
- 'AT4G16110'
- 'AT1G69780'
- 'AT1G52150'
- 'AT1G26960'
- 'AT4G32880'
- 'AT2G31650'
- 'AT4G00480'
- 'AT1G63650'
- 'AT3G25710'
- 'AT1G27740'
- 'AT2G24260'
- 'AT4G30980'
- 'AT5G58010'
- 'AT1G66470'
- 'AT4G33880'
- 'AT5G14170'
- 'AT3G06010'
- 'AT5G19310'
- 'AT2G46410'
- 'AT4G23750'
- 'AT2G39830'
- 'AT1G07640'
- 'AT2G37590'
- 'AT3G45610'
- 'AT5G02460'
- 'AT5G60200'
- 'AT5G62940'
- 'AT5G16780'
- 'AT5G22220'
- 'AT5G61600'
- 'AT5G56780'
- 'AT1G01380'
- 'AT1G66340'
- 'AT1G62310'
- 'AT5G11340'
- 'AT1G26870'
- 'AT1G14920'
- 'AT4G21060'
- 'AT5G25830'
- 'AT5G26930'
- 'AT1G68360'
- 'AT1G79840'
- 'AT5G41315'
- 'AT4G37740'
- 'AT2G36400'
- 'AT1G33240'
- 'AT3G54610'
- 'AT4G16780'
- 'AT1G73360'
- 'AT1G17920'
- 'AT1G05230'
- 'AT3G44750'
- 'AT1G13300'
- 'AT5G45710'
- 'AT1G04550'
- 'AT4G14550'
- 'AT2G46990'
- 'AT5G25890'
- 'AT3G17600'
- 'AT2G22670'
- 'AT2G02080'
- 'AT1G14580'
- 'AT5G03150'
- 'AT5G16560'
- 'AT1G62990'
- 'AT5G47390'
- 'AT2G40470'
- 'AT2G42430'
- 'AT2G45420'
- 'AT3G58190'
- 'AT4G00220'
- 'AT1G31320'
- 'AT1G62830'
- 'AT2G27230'
- 'AT5G12330'
- 'AT5G45420'
- 'AT1G03840'
- 'AT5G55580'
- 'AT1G14350'
- 'AT5G40330'
- 'AT5G57620'
- 'AT1G09540'
- 'AT2G02820'
- 'AT1G02250'
- 'AT1G12260'
- 'AT1G62700'
- 'AT1G71930'
- 'AT2G18060'
- 'AT3G03200'
- 'AT4G29230'
- 'AT4G36160'
- 'AT5G13180'
- 'AT5G17260'
- 'AT5G62380'
- 'AT5G64530'
- 'AT5G66300'
- 'AT5G39610'
- 'AT5G44160'
- 'AT2G25170'
- 'AT3G20840'
- 'AT1G51190'
- 'AT1G74500'
- 'AT2G12646'
- 'AT5G19790'
- 'AT1G53910'
- 'AT1G13260'
- 'AT3G12280'
- 'AT5G60690'
- 'AT3G60630'
- 'AT2G45160'
- 'AT4G00150'
- 'AT3G54220'
- 'AT1G79580'
- 'AT2G45680'
- 'AT5G53200'
- 'AT5G24520'
- 'AT2G47270'
- 'AT5G14750'
- 'AT3G57670'
- 'AT1G20700'
- 'AT5G13080'
- 'AT1G10480'
- 'AT1G67030'
- 'AT2G41940'
In [66]:
write.csv(data.frame(GeneID=gsgo),"./Gold_Standard_Root_TF_MINI_EX_StringDB.csv", quote=FALSE, row.names=FALSE)
R50¶
In [67]:
r50 <- 70
numz <- function(x){
sum(x==0)/length(x)
}
LIMA (GeneSys)¶
In [68]:
genesys <- dat_genesys
In [69]:
run_r50_genesys <- function(x){
genesys$combined_score <- min_max_normalize(rowSums(apply(genesys[,grep(x,colnames(genesys))],2,as.numeric)))
genesys <- genesys %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(genesys))){
if (genesys$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [70]:
## all cell type : in centrality + celltype & dev stage specificity
#dat$combined_score <- min_max_normalize(rowSums(apply(dat[,grep("in_centrality",colnames(dat))],2,as.numeric)))
#dat$celltype_specificity <- min_max_normalize(apply(apply(dat[,grep("in_centrality",colnames(dat))],2,as.numeric),1,numz))
#dat$weighted_score <- dat$combined_score + dat$celltype_specificity
#dat <- dat %>% arrange(desc(weighted_score))
#count <- 0
#for (i in seq(nrow(dat))){
# if (dat$GeneID[i] %in% gsgo){
# count <- count +1
# if (count == r50){
# print(i)
# break
# }
# }
#}
LIMA (scRNAseq)¶
In [71]:
raw <- dat_raw
In [72]:
run_r50_scRNAseq <- function(x){
raw$combined_score <- min_max_normalize(rowSums(apply(raw[,grep(x,colnames(raw))],2,as.numeric)))
raw <- raw %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(raw))){
if (raw$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
CellOracle¶
In [73]:
prepros <- function(x){
dat <- read.csv(x)
#dat <- dat %>% filter(role=="Connector Hub")
dat <- dat[,c(1, grep("centrality",colnames(dat)), 11)]
return(dat)
}
In [74]:
atri <- prepros("../celloracle/atrichoblast_X_root_integrated_celloracle_gene_score_iGRN.csv")
tri <- prepros("../celloracle/trichoblast_X_root_integrated_celloracle_gene_score_iGRN.csv")
lrc <- prepros("../celloracle/lrc_X_root_integrated_celloracle_gene_score_iGRN.csv")
cor <- prepros("../celloracle/cortex_X_root_integrated_celloracle_gene_score_iGRN.csv")
end <- prepros("../celloracle/endodermis_X_root_integrated_celloracle_gene_score_iGRN.csv")
per <- prepros("../celloracle/pericycle_X_root_integrated_celloracle_gene_score_iGRN.csv")
pro <- prepros("../celloracle/procambium_X_root_integrated_celloracle_gene_score_iGRN.csv")
xyl <- prepros("../celloracle/xylem_X_root_integrated_celloracle_gene_score_iGRN.csv")
phl <- prepros("../celloracle/phloem_X_root_integrated_celloracle_gene_score_iGRN.csv")
col <- prepros("../celloracle/columella_X_root_integrated_celloracle_gene_score_iGRN.csv")
In [75]:
dat <- rbind(atri, tri, lrc, cor, end, per, pro, xyl, phl, col)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all),
in_centrality = min_max_normalize(degree_centrality_in),
out_centrality = min_max_normalize(degree_centrality_out),
betweenness_centrality = min_max_normalize(betweenness_centrality),
#closeness_centrality = min_max_normalize(closeness_centrality),
eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality),
in_centrality = sum(in_centrality),
out_centrality = sum(out_centrality),
betweenness_centrality = sum(betweenness_centrality),
#closeness_centrality = sum(closeness_centrality),
eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:6],2,min_max_normalize))
In [76]:
head(dat)
| GeneID | degree_centrality | in_centrality | out_centrality | betweenness_centrality | eigenvector_centrality | |
|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | AT1G01010 | 0.149077052 | 0.11437038 | 0.12944779 | 0.032390482 | 0.138668256 |
| 2 | AT1G01030 | 0.034096371 | 0.08610945 | 0.01125146 | 0.004073877 | 0.030816983 |
| 3 | AT1G01160 | 0.002547637 | 0.01024930 | 0.00000000 | 0.000000000 | 0.001710741 |
| 4 | AT1G01260 | 0.228270857 | 0.11342176 | 0.20969750 | 0.095900756 | 0.185542460 |
| 5 | AT1G01350 | 0.045524426 | 0.15362458 | 0.00000000 | 0.000000000 | 0.031035276 |
| 6 | AT1G01380 | 0.042103043 | 0.10534045 | 0.02643830 | 0.001988356 | 0.051799125 |
In [77]:
celloracle_scRNAseq <- dat
In [78]:
atri <- prepros("../celloracle/atrichoblast_P_root_integrated_celloracle_gene_score_iGRN.csv")
tri <- prepros("../celloracle/trichoblast_P_root_integrated_celloracle_gene_score_iGRN.csv")
lrc <- prepros("../celloracle/lrc_P_root_integrated_celloracle_gene_score_iGRN.csv")
cor <- prepros("../celloracle/cortex_P_root_integrated_celloracle_gene_score_iGRN.csv")
end <- prepros("../celloracle/endodermis_P_root_integrated_celloracle_gene_score_iGRN.csv")
per <- prepros("../celloracle/pericycle_P_root_integrated_celloracle_gene_score_iGRN.csv")
pro <- prepros("../celloracle/procambium_P_root_integrated_celloracle_gene_score_iGRN.csv")
xyl <- prepros("../celloracle/xylem_P_root_integrated_celloracle_gene_score_iGRN.csv")
phl <- prepros("../celloracle/phloem_P_root_integrated_celloracle_gene_score_iGRN.csv")
col <- prepros("../celloracle/columella_P_root_integrated_celloracle_gene_score_iGRN.csv")
In [79]:
dat <- rbind(atri, tri, lrc, cor, end, per, pro, xyl, phl, col)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all),
in_centrality = min_max_normalize(degree_centrality_in),
out_centrality = min_max_normalize(degree_centrality_out),
betweenness_centrality = min_max_normalize(betweenness_centrality),
#closeness_centrality = min_max_normalize(closeness_centrality),
eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality),
in_centrality = sum(in_centrality),
out_centrality = sum(out_centrality),
betweenness_centrality = sum(betweenness_centrality),
#closeness_centrality = sum(closeness_centrality),
eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:6],2,min_max_normalize))
In [80]:
head(dat)
| GeneID | degree_centrality | in_centrality | out_centrality | betweenness_centrality | eigenvector_centrality | |
|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | AT1G01010 | 0.1439932022 | 0.079224788 | 0.12012398 | 0.011474823 | 0.1118648002 |
| 2 | AT1G01030 | 0.0411047792 | 0.069828417 | 0.01296914 | 0.006962497 | 0.0376800705 |
| 3 | AT1G01160 | 0.0007633751 | 0.002005498 | 0.00000000 | 0.000000000 | 0.0006013235 |
| 4 | AT1G01260 | 0.1635727775 | 0.011380966 | 0.20312406 | 0.056423197 | 0.1574869973 |
| 5 | AT1G01350 | 0.0118316898 | 0.029295749 | 0.00000000 | 0.000000000 | 0.0084215688 |
| 6 | AT1G01380 | 0.0625104141 | 0.112963058 | 0.03144836 | 0.003073585 | 0.0595090922 |
In [81]:
celloracle_GeneSys <- dat
In [82]:
run_r50_celloracle <- function(x, dat){
celloracle <- dat
celloracle$combined_score <- celloracle[,grep(x,colnames(celloracle))]
celloracle <- celloracle %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(celloracle))){
if (celloracle$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
Differential Expression Analysis¶
In [83]:
de_scRNAseq <- read.csv("Input_X_root_markers_ROC.csv")
In [84]:
de_scRNAseq <- de_scRNAseq %>% arrange(pct.diff_rank) %>% arrange(avg_diff_rank)%>% arrange(myAUC_rank)%>% arrange(combined_rank)
In [85]:
## Remove those not present in the dataset
de_scRNAseq <- de_scRNAseq[!is.na(match(de_scRNAseq$gene,exptf)),]
In [86]:
dat <- de_scRNAseq %>% group_by(gene) %>% reframe(myAUC_rank = mean(myAUC_rank),avg_diff_rank = mean(avg_diff_rank))
dat <- dat %>% arrange(myAUC_rank)
colnames(dat) <- c("GeneID","myAUC_rank","avg_diff_rank")
In [87]:
de_scRNAseq <- dat
In [88]:
de <- read.csv("GeneSys_P_root_markers_ROC.csv")
In [89]:
de <- de %>% arrange(pct.diff_rank) %>% arrange(avg_diff_rank)%>% arrange(myAUC_rank)%>% arrange(combined_rank)
In [90]:
## Remove those not present in the dataset
de <- de[!is.na(match(de$gene,exptf)),]
In [91]:
head(de)
| myAUC | avg_diff | power | avg_log2FC | pct.1 | pct.2 | cluster | gene | Name | n | pct.diff | pct.diff_rank | avg_diff_rank | myAUC_rank | combined_rank | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <int> | <int> | <chr> | <chr> | <chr> | <int> | <int> | <int> | <int> | <int> | <int> | |
| 11 | 0.983 | 0.7056382 | 0.966 | 1.0196951 | 1 | 1 | Atrichoblast_t2 | AT2G45050 | GATA2 | 9 | 0 | 1 | 3 | 1 | 1 |
| 15 | 0.975 | 0.6155794 | 0.950 | 0.8871276 | 1 | 1 | Columella_t8 | AT2G43000 | JUB1 | 9 | 0 | 1 | 4 | 1 | 1 |
| 17 | 0.982 | 0.6735658 | 0.964 | 0.9693554 | 1 | 1 | Columella_t9 | AT2G43000 | JUB1 | 9 | 0 | 2 | 4 | 1 | 1 |
| 18 | 0.991 | 0.9129982 | 0.982 | 1.3201464 | 1 | 1 | Trichoblast_t9 | AT5G58010 | BHLH82 | 6 | 0 | 3 | 4 | 1 | 1 |
| 30 | 0.992 | 0.9011193 | 0.984 | 1.2946407 | 1 | 1 | Xylem_t9 | AT1G62990 | KNAT7 | 5 | 0 | 3 | 23 | 1 | 1 |
| 33 | 0.979 | 0.8285899 | 0.958 | 1.1969267 | 1 | 1 | Phloem_t2 | AT1G79430 | APL | 10 | 0 | 2 | 1 | 2 | 1 |
In [92]:
dat <- de %>% group_by(gene) %>% reframe(myAUC_rank = mean(myAUC_rank),avg_diff_rank = mean(avg_diff_rank))
dat <- dat %>% arrange(myAUC_rank)
colnames(dat) <- c("GeneID","myAUC_rank","avg_diff_rank")
In [93]:
de_GeneSys <- dat
In [94]:
run_r50_de <- function(x, dat){
de <- dat
de$combined_score <- de[,grep(x,colnames(de))]
de <- de %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(de))){
if (de$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
Plot r50 with ranked by different centrality¶
In [95]:
## Expressed TFs permutation
R50_permutation <- c()
for (j in 1:1000){
ran <- sample(exptf, length(exptf), replace=FALSE)
count <- 0
for (i in seq(length(ran))){
if (ran[i] %in% gsgo){
count <- count +1
if (count == r50){
R50_permutation <- c(R50_permutation,i)
break
}
}
}
}
In [96]:
toplt <- data.frame(Methods=c("LIMA degree centrality (GeneSys)", "LIMA out centrality (GeneSys)", "LIMA in centrality (GeneSys)", "LIMA betweenness centrality (GeneSys)",
"LIMA eigenvector centrality (GeneSys)","LIMA degree centrality (scRNAseq)", "LIMA out centrality (scRNAseq)", "LIMA in centrality (scRNAseq)", "LIMA betweenness centrality (scRNAseq)",
"LIMA eigenvector centrality (scRNAseq)", "CellOracle degree centrality (scRNAseq)", "CellOracle out centrality (scRNAseq)",
"CellOracle in centrality (scRNAseq)", "CellOracle betweenness centrality (scRNAseq)", "CellOracle eigenvector centrality (scRNAseq)",
"CellOracle degree centrality (GeneSys)", "CellOracle out centrality (GeneSys)",
"CellOracle in centrality (GeneSys)", "CellOracle betweenness centrality (GeneSys)", "CellOracle eigenvector centrality (GeneSys)",
"DE myAUC rank (scRNAseq)", "DE avg diff rank (scRNAseq)", "DE myAUC rank (GeneSys)", "DE avg diff rank (GeneSys)", "Permutation"),
R50=c(run_r50_genesys('degree_centrality'),run_r50_genesys('out_centrality'),run_r50_genesys('in_centrality'),
run_r50_genesys('betweenness_centrality'),run_r50_genesys('eigenvector_centrality'),
run_r50_scRNAseq('degree_centrality'),run_r50_scRNAseq('out_centrality'),run_r50_scRNAseq('in_centrality'),
run_r50_scRNAseq('betweenness_centrality'),run_r50_scRNAseq('eigenvector_centrality'),
run_r50_celloracle('degree_centrality', celloracle_scRNAseq),run_r50_celloracle('out_centrality', celloracle_scRNAseq),run_r50_celloracle('in_centrality', celloracle_scRNAseq),
run_r50_celloracle('betweenness_centrality', celloracle_scRNAseq),run_r50_celloracle('eigenvector_centrality', celloracle_scRNAseq),
run_r50_celloracle('degree_centrality', celloracle_GeneSys),run_r50_celloracle('out_centrality', celloracle_GeneSys),run_r50_celloracle('in_centrality', celloracle_GeneSys),
run_r50_celloracle('betweenness_centrality', celloracle_GeneSys),run_r50_celloracle('eigenvector_centrality', celloracle_GeneSys),
run_r50_de('myAUC_rank', de_scRNAseq),run_r50_de('avg_diff_rank', de_scRNAseq),run_r50_de('myAUC_rank', de_GeneSys),run_r50_de('avg_diff_rank', de_GeneSys), mean(R50_permutation)))
In [97]:
toplt
| Methods | R50 |
|---|---|
| <chr> | <dbl> |
| LIMA degree centrality (GeneSys) | 314.000 |
| LIMA out centrality (GeneSys) | 327.000 |
| LIMA in centrality (GeneSys) | 375.000 |
| LIMA betweenness centrality (GeneSys) | 335.000 |
| LIMA eigenvector centrality (GeneSys) | 432.000 |
| LIMA degree centrality (scRNAseq) | 365.000 |
| LIMA out centrality (scRNAseq) | 330.000 |
| LIMA in centrality (scRNAseq) | 444.000 |
| LIMA betweenness centrality (scRNAseq) | 447.000 |
| LIMA eigenvector centrality (scRNAseq) | 536.000 |
| CellOracle degree centrality (scRNAseq) | 470.000 |
| CellOracle out centrality (scRNAseq) | 513.000 |
| CellOracle in centrality (scRNAseq) | 517.000 |
| CellOracle betweenness centrality (scRNAseq) | 410.000 |
| CellOracle eigenvector centrality (scRNAseq) | 453.000 |
| CellOracle degree centrality (GeneSys) | 434.000 |
| CellOracle out centrality (GeneSys) | 466.000 |
| CellOracle in centrality (GeneSys) | 312.000 |
| CellOracle betweenness centrality (GeneSys) | 335.000 |
| CellOracle eigenvector centrality (GeneSys) | 445.000 |
| DE myAUC rank (scRNAseq) | 380.000 |
| DE avg diff rank (scRNAseq) | 403.000 |
| DE myAUC rank (GeneSys) | 310.000 |
| DE avg diff rank (GeneSys) | 331.000 |
| Permutation | 743.406 |
In [98]:
options(repr.plot.width=10, repr.plot.height=8)
ggplot(toplt, aes(x=reorder(Methods, R50, decreasing = TRUE), y=R50)) + geom_point(size=4)+
labs(title="TF Prioritization Performance",x="", y = "R50")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [99]:
toplt <- data.frame(Methods=factor(c(rep("LIMA (GeneSys)",5), rep("LIMA (scRNAseq)",5), rep("CellOracle (scRNAseq)",5), rep("CellOracle (GeneSys)",5), rep("DE (scRNAseq)",2), rep("DE (GeneSys)",2), "Permutation"),
levels=c("Permutation", "CellOracle (scRNAseq)", "LIMA (scRNAseq)", "CellOracle (GeneSys)", "DE (scRNAseq)", "LIMA (GeneSys)", "DE (GeneSys)")), R50=toplt$R50)
options(repr.plot.width=6, repr.plot.height=5)
ggplot(toplt, aes(x=Methods, y=R50))+
geom_boxplot(width=0.4)+
labs(title="TF Prioritization Performance",x="", y = "R50")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [100]:
toplt <- data.frame(Methods=factor(c(rep("LIMA (GeneSys)",5), rep("LIMA (scRNAseq)",5), rep("CellOracle (scRNAseq)",5), rep("CellOracle (GeneSys)",5), rep("DE (scRNAseq)",2), rep("DE (GeneSys)",2), "Permutation"),
levels=c("Permutation", "CellOracle (scRNAseq)", "CellOracle (GeneSys)", "LIMA (scRNAseq)", "LIMA (GeneSys)", "DE (scRNAseq)", "DE (GeneSys)")), R50=toplt$R50)
options(repr.plot.width=6, repr.plot.height=5)
ggplot(toplt, aes(x=Methods, y=R50))+
geom_boxplot(width=0.4)+
labs(title="TF Prioritization Performance",x="", y = "R50")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [101]:
toplt <- data.frame(Methods=factor(c(rep("LIMA (GeneSys)",5), rep("LIMA (scRNAseq)",5), rep("CellOracle (scRNAseq)",5), rep("CellOracle (GeneSys)",5), rep("DE (scRNAseq)",2), rep("DE (GeneSys)",2), "Permutation"),
levels=c("Permutation", "CellOracle (scRNAseq)", "LIMA (scRNAseq)", "DE (scRNAseq)", "CellOracle (GeneSys)", "LIMA (GeneSys)", "DE (GeneSys)")), R50=toplt$R50)
options(repr.plot.width=6, repr.plot.height=5)
ggplot(toplt, aes(x=Methods, y=R50))+
geom_boxplot(width=0.4)+
labs(title="TF Prioritization Performance",x="", y = "R50")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
R50 for Tissue specific TFs¶
Stele (xylem, phloem, procambium)¶
In [102]:
gsgo_miniex_stringdb <- gsgo
In [103]:
gsgo <- read.csv("./Gold_Standard_Root_TF_StringDB.csv", header=TRUE)
gsgo <- gsgo[which(gsgo[,5]=="Yes"),]
gsgo$preferredName
gsgo <- gsgo$GeneID
gsgo <- intersect(gsgo, gsgo_miniex_stringdb)
- 'APL'
- 'ATHB-8'
- 'BHLH48'
- 'DAR2'
- 'DOF5.3'
- 'DOF5.6'
- 'DOT2'
- 'ERF104'
- 'ET2'
- 'GATA12'
- 'KAN1'
- 'KNAT7'
- 'LBD15'
- 'LBD18'
- 'LBD30'
- 'LBD4'
- 'LHW'
- 'MYB20'
- 'MYB32'
- 'MYB43'
- 'MYB46'
- 'MYB52'
- 'MYB61'
- 'MYB7'
- 'MYB83'
- 'MYB85'
- 'NAC005'
- 'NAC007'
- 'NAC010'
- 'NAC026'
- 'NAC030'
- 'NAC037'
- 'NAC043'
- 'NAC045'
- 'NAC075'
- 'NAC076'
- 'NAC083'
- 'NAC086'
- 'NAC101'
- 'NAC104'
- 'NAC105'
- 'REV'
- 'WOX14'
In [104]:
length(gsgo)
32
In [105]:
r50 <- 16
In [106]:
## Define genesys
run_r50_genesys <- function(x){
genesys$ct_score <- min_max_normalize(rowSums(apply(genesys[,grep(paste0('xyl_',x,'|phl_',x,'|pro_',x),colnames(genesys))],2,as.numeric)))
genesys$combined_score <- genesys$ct_score
genesys <- genesys %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(genesys))){
if (genesys$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [107]:
## Define raw
run_r50_scRNAseq <- function(x){
raw$ct_score <- min_max_normalize(rowSums(apply(raw[,grep(paste0('xyl_',x,'|phl_',x,'|pro_',x),colnames(raw))],2,as.numeric)))
raw$combined_score <- raw$ct_score
raw <- raw %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(raw))){
if (raw$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [108]:
## Define celloracle
pro <- prepros("../celloracle/procambium_X_root_integrated_celloracle_gene_score_iGRN.csv")
xyl <- prepros("../celloracle/xylem_X_root_integrated_celloracle_gene_score_iGRN.csv")
phl <- prepros("../celloracle/phloem_X_root_integrated_celloracle_gene_score_iGRN.csv")
## Define celloracle
dat <- rbind(pro, xyl, phl)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all),
in_centrality = min_max_normalize(degree_centrality_in),
out_centrality = min_max_normalize(degree_centrality_out),
betweenness_centrality = min_max_normalize(betweenness_centrality),
eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality),
in_centrality = sum(in_centrality),
out_centrality = sum(out_centrality),
betweenness_centrality = sum(betweenness_centrality),
eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:6],2,min_max_normalize))
celloracle_scRNAseq <- dat
## Define celloracle
pro <- prepros("../celloracle/procambium_P_root_integrated_celloracle_gene_score_iGRN.csv")
xyl <- prepros("../celloracle/xylem_P_root_integrated_celloracle_gene_score_iGRN.csv")
phl <- prepros("../celloracle/phloem_P_root_integrated_celloracle_gene_score_iGRN.csv")
## Define celloracle
dat <- rbind(pro, xyl, phl)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all),
in_centrality = min_max_normalize(degree_centrality_in),
out_centrality = min_max_normalize(degree_centrality_out),
betweenness_centrality = min_max_normalize(betweenness_centrality),
eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality),
in_centrality = sum(in_centrality),
out_centrality = sum(out_centrality),
betweenness_centrality = sum(betweenness_centrality),
eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:6],2,min_max_normalize))
celloracle_GeneSys <- dat
run_r50_celloracle <- function(x, dat){
celloracle <- dat
celloracle$combined_score <- celloracle[,grep(x,colnames(celloracle))]
celloracle <- celloracle %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(celloracle))){
if (celloracle$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [109]:
de <- read.csv("Input_X_root_markers_ROC.csv")
In [110]:
## Define DE
de <- read.csv("Input_X_root_markers_ROC.csv")
de <- de[c(grep("Xylem",de$cluster),grep("Phloem",de$cluster),grep("Procambium",de$cluster)),]
de <- de[!is.na(match(de$gene,exptf)),]
dat <- de %>% group_by(gene) %>% reframe(myAUC_rank = mean(myAUC_rank),avg_diff_rank = mean(avg_diff_rank))
colnames(dat) <- c("GeneID","myAUC_rank","avg_diff_rank")
de_scRNAseq <- dat
In [111]:
de <- read.csv("GeneSys_P_root_markers_ROC.csv")
de <- de[c(grep("Xylem",de$cluster),grep("Phloem",de$cluster),grep("Procambium",de$cluster)),]
de <- de[!is.na(match(de$gene,exptf)),]
dat <- de %>% group_by(gene) %>% reframe(myAUC_rank = mean(myAUC_rank),avg_diff_rank = mean(avg_diff_rank))
colnames(dat) <- c("GeneID","myAUC_rank","avg_diff_rank")
de_GeneSys <- dat
In [112]:
run_r50_de <- function(x, dat){
de <- dat
de$combined_score <- de[,grep(x,colnames(de))]
de <- de %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(de))){
if (de$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [113]:
## Expressed TFs permutation
R50_permutation <- c()
for (j in 1:1000){
ran <- sample(exptf, length(exptf), replace=FALSE)
count <- 0
for (i in seq(length(ran))){
if (ran[i] %in% gsgo){
count <- count +1
if (count == r50){
R50_permutation <- c(R50_permutation,i)
break
}
}
}
}
In [114]:
toplt <- data.frame(Methods=c("LIMA degree centrality (GeneSys)", "LIMA out centrality (GeneSys)", "LIMA in centrality (GeneSys)", "LIMA betweenness centrality (GeneSys)",
"LIMA eigenvector centrality (GeneSys)","LIMA degree centrality (scRNAseq)", "LIMA out centrality (scRNAseq)", "LIMA in centrality (scRNAseq)", "LIMA betweenness centrality (scRNAseq)",
"LIMA eigenvector centrality (scRNAseq)", "CellOracle degree centrality (scRNAseq)", "CellOracle out centrality (scRNAseq)",
"CellOracle in centrality (scRNAseq)", "CellOracle betweenness centrality (scRNAseq)", "CellOracle eigenvector centrality (scRNAseq)",
"CellOracle degree centrality (GeneSys)", "CellOracle out centrality (GeneSys)",
"CellOracle in centrality (GeneSys)", "CellOracle betweenness centrality (GeneSys)", "CellOracle eigenvector centrality (GeneSys)",
"DE myAUC rank (scRNAseq)", "DE avg diff rank (scRNAseq)", "DE myAUC rank (GeneSys)", "DE avg diff rank (GeneSys)", "Permutation"),
R50=c(run_r50_genesys('degree_centrality'),run_r50_genesys('out_centrality'),run_r50_genesys('in_centrality'),
run_r50_genesys('betweenness_centrality'),run_r50_genesys('eigenvector_centrality'),
run_r50_scRNAseq('degree_centrality'),run_r50_scRNAseq('out_centrality'),run_r50_scRNAseq('in_centrality'),
run_r50_scRNAseq('betweenness_centrality'),run_r50_scRNAseq('eigenvector_centrality'),
run_r50_celloracle('degree_centrality', celloracle_scRNAseq),run_r50_celloracle('out_centrality', celloracle_scRNAseq),run_r50_celloracle('in_centrality', celloracle_scRNAseq),
run_r50_celloracle('betweenness_centrality', celloracle_scRNAseq),run_r50_celloracle('eigenvector_centrality', celloracle_scRNAseq),
run_r50_celloracle('degree_centrality', celloracle_GeneSys),run_r50_celloracle('out_centrality', celloracle_GeneSys),run_r50_celloracle('in_centrality', celloracle_GeneSys),
run_r50_celloracle('betweenness_centrality', celloracle_GeneSys),run_r50_celloracle('eigenvector_centrality', celloracle_GeneSys),
run_r50_de('myAUC_rank', de_scRNAseq),run_r50_de('avg_diff_rank', de_scRNAseq),run_r50_de('myAUC_rank', de_GeneSys),run_r50_de('avg_diff_rank', de_GeneSys), mean(R50_permutation)))
In [115]:
toplt
| Methods | R50 |
|---|---|
| <chr> | <dbl> |
| LIMA degree centrality (GeneSys) | 125.000 |
| LIMA out centrality (GeneSys) | 86.000 |
| LIMA in centrality (GeneSys) | 205.000 |
| LIMA betweenness centrality (GeneSys) | 119.000 |
| LIMA eigenvector centrality (GeneSys) | 165.000 |
| LIMA degree centrality (scRNAseq) | 122.000 |
| LIMA out centrality (scRNAseq) | 86.000 |
| LIMA in centrality (scRNAseq) | 164.000 |
| LIMA betweenness centrality (scRNAseq) | 200.000 |
| LIMA eigenvector centrality (scRNAseq) | 212.000 |
| CellOracle degree centrality (scRNAseq) | 261.000 |
| CellOracle out centrality (scRNAseq) | 318.000 |
| CellOracle in centrality (scRNAseq) | 148.000 |
| CellOracle betweenness centrality (scRNAseq) | 111.000 |
| CellOracle eigenvector centrality (scRNAseq) | 217.000 |
| CellOracle degree centrality (GeneSys) | 203.000 |
| CellOracle out centrality (GeneSys) | 250.000 |
| CellOracle in centrality (GeneSys) | 94.000 |
| CellOracle betweenness centrality (GeneSys) | 95.000 |
| CellOracle eigenvector centrality (GeneSys) | 183.000 |
| DE myAUC rank (scRNAseq) | 163.000 |
| DE avg diff rank (scRNAseq) | 179.000 |
| DE myAUC rank (GeneSys) | 132.000 |
| DE avg diff rank (GeneSys) | 142.000 |
| Permutation | 727.653 |
In [116]:
options(repr.plot.width=10, repr.plot.height=8)
ggplot(toplt, aes(x=reorder(Methods, R50, decreasing = TRUE), y=R50)) + geom_point(size=4)+
labs(title="Stele-specific TF Prioritization Performance",x="", y = "R50")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [117]:
toplt <- data.frame(Methods=factor(c(rep("LIMA (GeneSys)",5), rep("LIMA (scRNAseq)",5), rep("CellOracle (scRNAseq)",5), rep("CellOracle (GeneSys)",5), rep("DE (scRNAseq)",2), rep("DE (GeneSys)",2), "Permutation"),
levels=c("Permutation", "CellOracle (scRNAseq)", "DE (scRNAseq)", "LIMA (scRNAseq)", "CellOracle (GeneSys)", "DE (GeneSys)", "LIMA (GeneSys)")), R50=toplt$R50)
options(repr.plot.width=6, repr.plot.height=5)
ggplot(toplt, aes(x=Methods, y=R50))+
geom_boxplot(width=0.4)+
labs(title="Stele-specific TF Prioritization Performance",x="", y = "R50")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
Epidermis¶
In [118]:
gsgo <- read.csv("./Gold_Standard_Root_TF_StringDB.csv", header=TRUE)
gsgo <- gsgo[which(gsgo[,6]=="Yes"),]
gsgo$preferredName
gsgo <- gsgo$GeneID
gsgo <- intersect(gsgo, gsgo_miniex_stringdb)
- 'AGL16'
- 'AL6'
- 'ANL2'
- 'BHLH12'
- 'BHLH2'
- 'BHLH32'
- 'BHLH54'
- 'BHLH66'
- 'BHLH69'
- 'BHLH82'
- 'BHLH83'
- 'BHLH85'
- 'BHLH86'
- 'CPC'
- 'E2FB'
- 'ETC1'
- 'GALT2'
- 'GIS3'
- 'GL2'
- 'GL3'
- 'GTL1'
- 'HDG11'
- 'HDG12'
- 'HDG2'
- 'JMJ25'
- 'MAMYB'
- 'MYB124'
- 'MYB23'
- 'MYB86'
- 'MYB88'
- 'MYC3'
- 'MYC4'
- 'RBR1'
- 'SCL22'
- 'SCL27'
- 'SCL6'
- 'SCRM'
- 'TCX2'
- 'TCX3'
- 'TRY'
- 'TSO1'
- 'TTG1'
- 'WER'
- 'WRKY44'
- 'WRKY75'
- 'ZFP5'
- 'ZFP6'
- 'ZFP8'
In [119]:
## R35
length(gsgo)
r50 <- 13
37
In [120]:
## Define genesys
run_r50_genesys <- function(x){
genesys$ct_score <- min_max_normalize(rowSums(apply(genesys[,grep(paste0('atri_',x,'|tri_',x,'|lrc_',x),colnames(genesys))],2,as.numeric)))
genesys$combined_score <- genesys$ct_score
genesys <- genesys %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(genesys))){
if (genesys$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [121]:
## Define raw
run_r50_scRNAseq <- function(x){
raw$ct_score <- min_max_normalize(rowSums(apply(raw[,grep(paste0('atri_',x,'|tri_',x,'|lrc_',x),colnames(raw))],2,as.numeric)))
raw$combined_score <- raw$ct_score
raw <- raw %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(raw))){
if (raw$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [122]:
## Define celloracle
atri <- prepros("../celloracle/atrichoblast_X_root_integrated_celloracle_gene_score_iGRN.csv")
tri <- prepros("../celloracle/trichoblast_X_root_integrated_celloracle_gene_score_iGRN.csv")
lrc <- prepros("../celloracle/lrc_X_root_integrated_celloracle_gene_score_iGRN.csv")
## Define celloracle
dat <- rbind(atri, tri, lrc)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all),
in_centrality = min_max_normalize(degree_centrality_in),
out_centrality = min_max_normalize(degree_centrality_out),
betweenness_centrality = min_max_normalize(betweenness_centrality),
eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality),
in_centrality = sum(in_centrality),
out_centrality = sum(out_centrality),
betweenness_centrality = sum(betweenness_centrality),
eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:6],2,min_max_normalize))
celloracle_scRNAseq <- dat
## Define celloracle
atri <- prepros("../celloracle/atrichoblast_P_root_integrated_celloracle_gene_score_iGRN.csv")
tri <- prepros("../celloracle/trichoblast_P_root_integrated_celloracle_gene_score_iGRN.csv")
lrc <- prepros("../celloracle/lrc_P_root_integrated_celloracle_gene_score_iGRN.csv")
## Define celloracle
dat <- rbind(atri, tri, lrc)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all),
in_centrality = min_max_normalize(degree_centrality_in),
out_centrality = min_max_normalize(degree_centrality_out),
betweenness_centrality = min_max_normalize(betweenness_centrality),
eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality),
in_centrality = sum(in_centrality),
out_centrality = sum(out_centrality),
betweenness_centrality = sum(betweenness_centrality),
eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:6],2,min_max_normalize))
celloracle_GeneSys <- dat
run_r50_celloracle <- function(x, dat){
celloracle <- dat
celloracle$combined_score <- celloracle[,grep(x,colnames(celloracle))]
celloracle <- celloracle %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(celloracle))){
if (celloracle$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [123]:
## Define DE
de <- read.csv("Input_X_root_markers_ROC.csv")
de <- de[c(grep("Atrichoblast",de$cluster),grep("Trichoblast",de$cluster),grep("Lateral Root Cap",de$cluster)),]
de <- de[!is.na(match(de$gene,exptf)),]
dat <- de %>% group_by(gene) %>% reframe(myAUC_rank = mean(myAUC_rank),avg_diff_rank = mean(avg_diff_rank))
colnames(dat) <- c("GeneID","myAUC_rank","avg_diff_rank")
de_scRNAseq <- dat
de <- read.csv("GeneSys_P_root_markers_ROC.csv")
de <- de[c(grep("Atrichoblast",de$cluster),grep("Trichoblast",de$cluster),grep("Lateral Root Cap",de$cluster)),]
de <- de[!is.na(match(de$gene,exptf)),]
dat <- de %>% group_by(gene) %>% reframe(myAUC_rank = mean(myAUC_rank),avg_diff_rank = mean(avg_diff_rank))
colnames(dat) <- c("GeneID","myAUC_rank","avg_diff_rank")
de_GeneSys <- dat
run_r50_de <- function(x, dat){
de <- dat
de$combined_score <- de[,grep(x,colnames(de))]
de <- de %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(de))){
if (de$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [124]:
## Expressed TFs permutation
R50_permutation <- c()
for (j in 1:1000){
ran <- sample(exptf, length(exptf), replace=FALSE)
count <- 0
for (i in seq(length(ran))){
if (ran[i] %in% gsgo){
count <- count +1
if (count == r50){
R50_permutation <- c(R50_permutation,i)
break
}
}
}
}
In [125]:
length(intersect(de_GeneSys$GeneID, gsgo))
13
In [126]:
toplt <- data.frame(Methods=c("LIMA degree centrality (GeneSys)", "LIMA out centrality (GeneSys)", "LIMA in centrality (GeneSys)", "LIMA betweenness centrality (GeneSys)",
"LIMA eigenvector centrality (GeneSys)","LIMA degree centrality (scRNAseq)", "LIMA out centrality (scRNAseq)", "LIMA in centrality (scRNAseq)", "LIMA betweenness centrality (scRNAseq)",
"LIMA eigenvector centrality (scRNAseq)", "CellOracle degree centrality (scRNAseq)", "CellOracle out centrality (scRNAseq)",
"CellOracle in centrality (scRNAseq)", "CellOracle betweenness centrality (scRNAseq)", "CellOracle eigenvector centrality (scRNAseq)",
"CellOracle degree centrality (GeneSys)", "CellOracle out centrality (GeneSys)",
"CellOracle in centrality (GeneSys)", "CellOracle betweenness centrality (GeneSys)", "CellOracle eigenvector centrality (GeneSys)",
"DE myAUC rank (scRNAseq)", "DE avg diff rank (scRNAseq)", "DE myAUC rank (GeneSys)", "DE avg diff rank (GeneSys)", "Permutation"),
R50=c(run_r50_genesys('degree_centrality'),run_r50_genesys('out_centrality'),run_r50_genesys('in_centrality'),
run_r50_genesys('betweenness_centrality'),run_r50_genesys('eigenvector_centrality'),
run_r50_scRNAseq('degree_centrality'),run_r50_scRNAseq('out_centrality'),run_r50_scRNAseq('in_centrality'),
run_r50_scRNAseq('betweenness_centrality'),run_r50_scRNAseq('eigenvector_centrality'),
run_r50_celloracle('degree_centrality', celloracle_scRNAseq),run_r50_celloracle('out_centrality', celloracle_scRNAseq),run_r50_celloracle('in_centrality', celloracle_scRNAseq),
run_r50_celloracle('betweenness_centrality', celloracle_scRNAseq),run_r50_celloracle('eigenvector_centrality', celloracle_scRNAseq),
run_r50_celloracle('degree_centrality', celloracle_GeneSys),run_r50_celloracle('out_centrality', celloracle_GeneSys),run_r50_celloracle('in_centrality', celloracle_GeneSys),
run_r50_celloracle('betweenness_centrality', celloracle_GeneSys),run_r50_celloracle('eigenvector_centrality', celloracle_GeneSys),
run_r50_de('myAUC_rank', de_scRNAseq),run_r50_de('avg_diff_rank', de_scRNAseq),run_r50_de('myAUC_rank', de_GeneSys),run_r50_de('avg_diff_rank', de_GeneSys), mean(R50_permutation)))
In [127]:
toplt
| Methods | R50 |
|---|---|
| <chr> | <dbl> |
| LIMA degree centrality (GeneSys) | 105.000 |
| LIMA out centrality (GeneSys) | 148.000 |
| LIMA in centrality (GeneSys) | 108.000 |
| LIMA betweenness centrality (GeneSys) | 81.000 |
| LIMA eigenvector centrality (GeneSys) | 142.000 |
| LIMA degree centrality (scRNAseq) | 112.000 |
| LIMA out centrality (scRNAseq) | 120.000 |
| LIMA in centrality (scRNAseq) | 142.000 |
| LIMA betweenness centrality (scRNAseq) | 100.000 |
| LIMA eigenvector centrality (scRNAseq) | 170.000 |
| CellOracle degree centrality (scRNAseq) | 270.000 |
| CellOracle out centrality (scRNAseq) | 352.000 |
| CellOracle in centrality (scRNAseq) | 131.000 |
| CellOracle betweenness centrality (scRNAseq) | 163.000 |
| CellOracle eigenvector centrality (scRNAseq) | 301.000 |
| CellOracle degree centrality (GeneSys) | 296.000 |
| CellOracle out centrality (GeneSys) | 406.000 |
| CellOracle in centrality (GeneSys) | 141.000 |
| CellOracle betweenness centrality (GeneSys) | 222.000 |
| CellOracle eigenvector centrality (GeneSys) | 309.000 |
| DE myAUC rank (scRNAseq) | 91.000 |
| DE avg diff rank (scRNAseq) | 113.000 |
| DE myAUC rank (GeneSys) | 81.000 |
| DE avg diff rank (GeneSys) | 92.000 |
| Permutation | 510.154 |
In [128]:
options(repr.plot.width=10, repr.plot.height=8)
ggplot(toplt, aes(x=reorder(Methods, R50, decreasing = TRUE), y=R50)) + geom_point(size=4)+
labs(title="Epidermis-specific TF Prioritization Performance",x="", y = "R35")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [129]:
toplt <- data.frame(Methods=factor(c(rep("LIMA (GeneSys)",5), rep("LIMA (scRNAseq)",5), rep("CellOracle (scRNAseq)",5), rep("CellOracle (GeneSys)",5), rep("DE (scRNAseq)",2), rep("DE (GeneSys)",2), "Permutation"),
levels=c("Permutation", "CellOracle (scRNAseq)", "LIMA (scRNAseq)", "DE (scRNAseq)", "CellOracle (GeneSys)", "LIMA (GeneSys)", "DE (GeneSys)")), R50=toplt$R50)
options(repr.plot.width=6, repr.plot.height=5)
ggplot(toplt, aes(x=Methods, y=R50))+
geom_boxplot(width=0.4)+
labs(title="Epidermis-specific TF Prioritization Performance",x="", y = "R35")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
R50 for celltype specific¶
Xylem¶
In [130]:
gsgo <- read.csv("./Gold_Standard_Root_TF_StringDB.csv", header=TRUE)
gsgo <- gsgo[which(gsgo[,7]=="Yes"),]
gsgo$preferredName
gsgo <- gsgo$GeneID
gsgo <- intersect(gsgo, gsgo_miniex_stringdb)
- 'APL'
- 'ATHB-8'
- 'BHLH48'
- 'ET2'
- 'KNAT7'
- 'LBD18'
- 'LBD30'
- 'LHW'
- 'MYB20'
- 'MYB32'
- 'MYB43'
- 'MYB46'
- 'MYB52'
- 'MYB61'
- 'MYB7'
- 'MYB83'
- 'MYB85'
- 'NAC005'
- 'NAC007'
- 'NAC010'
- 'NAC026'
- 'NAC030'
- 'NAC037'
- 'NAC043'
- 'NAC076'
- 'NAC083'
- 'NAC101'
- 'NAC104'
- 'NAC105'
- 'REV'
In [131]:
length(gsgo)
r50 <- 10
19
In [132]:
## Define genesys
run_r50_genesys <- function(x){
genesys$ct_score <- min_max_normalize(rowSums(apply(genesys[,grep(paste0('xyl_',x),colnames(genesys))],2,as.numeric)))
genesys$combined_score <- genesys$ct_score
genesys <- genesys %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(genesys))){
if (genesys$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [133]:
## Define genesys
run_r50_scRNAseq <- function(x){
raw$ct_score <- min_max_normalize(rowSums(apply(raw[,grep(paste0('xyl_',x),colnames(raw))],2,as.numeric)))
raw$combined_score <- raw$ct_score
raw <- raw %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(raw))){
if (raw$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [134]:
## Define celloracle
xyl <- prepros("../celloracle/xylem_X_root_integrated_celloracle_gene_score_iGRN.csv")
## Define celloracle
dat <- rbind(xyl)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all),
in_centrality = min_max_normalize(degree_centrality_in),
out_centrality = min_max_normalize(degree_centrality_out),
betweenness_centrality = min_max_normalize(betweenness_centrality),
eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality),
in_centrality = sum(in_centrality),
out_centrality = sum(out_centrality),
betweenness_centrality = sum(betweenness_centrality),
eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:6],2,min_max_normalize))
celloracle_scRNAseq <- dat
## Define celloracle
xyl <- prepros("../celloracle/xylem_P_root_integrated_celloracle_gene_score_iGRN.csv")
## Define celloracle
dat <- rbind(xyl)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all),
in_centrality = min_max_normalize(degree_centrality_in),
out_centrality = min_max_normalize(degree_centrality_out),
betweenness_centrality = min_max_normalize(betweenness_centrality),
eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality),
in_centrality = sum(in_centrality),
out_centrality = sum(out_centrality),
betweenness_centrality = sum(betweenness_centrality),
eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:6],2,min_max_normalize))
celloracle_GeneSys <- dat
run_r50_celloracle <- function(x, dat){
celloracle <- dat
celloracle$combined_score <- celloracle[,grep(x,colnames(celloracle))]
celloracle <- celloracle %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(celloracle))){
if (celloracle$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [135]:
## Define DE
de <- read.csv("Input_X_root_markers_ROC.csv")
de <- de[c(grep("Xylem",de$cluster)),]
de <- de[!is.na(match(de$gene,exptf)),]
dat <- de %>% group_by(gene) %>% reframe(myAUC_rank = mean(myAUC_rank),avg_diff_rank = mean(avg_diff_rank))
colnames(dat) <- c("GeneID","myAUC_rank","avg_diff_rank")
de_scRNAseq <- dat
de <- read.csv("GeneSys_P_root_markers_ROC.csv")
de <- de[c(grep("Xylem",de$cluster)),]
de <- de[!is.na(match(de$gene,exptf)),]
dat <- de %>% group_by(gene) %>% reframe(myAUC_rank = mean(myAUC_rank),avg_diff_rank = mean(avg_diff_rank))
colnames(dat) <- c("GeneID","myAUC_rank","avg_diff_rank")
de_GeneSys <- dat
run_r50_de <- function(x, dat){
de <- dat
de$combined_score <- de[,grep(x,colnames(de))]
de <- de %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(de))){
if (de$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [136]:
## Expressed TFs permutation
R50_permutation <- c()
for (j in 1:1000){
ran <- sample(exptf, length(exptf), replace=FALSE)
count <- 0
for (i in seq(length(ran))){
if (ran[i] %in% gsgo){
count <- count +1
if (count == r50){
R50_permutation <- c(R50_permutation,i)
break
}
}
}
}
In [137]:
toplt <- data.frame(Methods=c("LIMA degree centrality (GeneSys)", "LIMA out centrality (GeneSys)", "LIMA in centrality (GeneSys)", "LIMA betweenness centrality (GeneSys)",
"LIMA eigenvector centrality (GeneSys)","LIMA degree centrality (scRNAseq)", "LIMA out centrality (scRNAseq)", "LIMA in centrality (scRNAseq)", "LIMA betweenness centrality (scRNAseq)",
"LIMA eigenvector centrality (scRNAseq)", "CellOracle degree centrality (scRNAseq)", "CellOracle out centrality (scRNAseq)",
"CellOracle in centrality (scRNAseq)", "CellOracle betweenness centrality (scRNAseq)", "CellOracle eigenvector centrality (scRNAseq)",
"CellOracle degree centrality (GeneSys)", "CellOracle out centrality (GeneSys)",
"CellOracle in centrality (GeneSys)", "CellOracle betweenness centrality (GeneSys)", "CellOracle eigenvector centrality (GeneSys)",
"DE myAUC rank (scRNAseq)", "DE avg diff rank (scRNAseq)", "DE myAUC rank (GeneSys)", "DE avg diff rank (GeneSys)", "Permutation"),
R50=c(run_r50_genesys('degree_centrality'),run_r50_genesys('out_centrality'),run_r50_genesys('in_centrality'),
run_r50_genesys('betweenness_centrality'),run_r50_genesys('eigenvector_centrality'),
run_r50_scRNAseq('degree_centrality'),run_r50_scRNAseq('out_centrality'),run_r50_scRNAseq('in_centrality'),
run_r50_scRNAseq('betweenness_centrality'),run_r50_scRNAseq('eigenvector_centrality'),
run_r50_celloracle('degree_centrality', celloracle_scRNAseq),run_r50_celloracle('out_centrality', celloracle_scRNAseq),run_r50_celloracle('in_centrality', celloracle_scRNAseq),
run_r50_celloracle('betweenness_centrality', celloracle_scRNAseq),run_r50_celloracle('eigenvector_centrality', celloracle_scRNAseq),
run_r50_celloracle('degree_centrality', celloracle_GeneSys),run_r50_celloracle('out_centrality', celloracle_GeneSys),run_r50_celloracle('in_centrality', celloracle_GeneSys),
run_r50_celloracle('betweenness_centrality', celloracle_GeneSys),run_r50_celloracle('eigenvector_centrality', celloracle_GeneSys),
run_r50_de('myAUC_rank', de_scRNAseq),run_r50_de('avg_diff_rank', de_scRNAseq),run_r50_de('myAUC_rank', de_GeneSys),run_r50_de('avg_diff_rank', de_GeneSys), mean(R50_permutation)))
In [138]:
toplt
| Methods | R50 |
|---|---|
| <chr> | <dbl> |
| LIMA degree centrality (GeneSys) | 21.000 |
| LIMA out centrality (GeneSys) | 19.000 |
| LIMA in centrality (GeneSys) | 42.000 |
| LIMA betweenness centrality (GeneSys) | 52.000 |
| LIMA eigenvector centrality (GeneSys) | 23.000 |
| LIMA degree centrality (scRNAseq) | 22.000 |
| LIMA out centrality (scRNAseq) | 22.000 |
| LIMA in centrality (scRNAseq) | 25.000 |
| LIMA betweenness centrality (scRNAseq) | 107.000 |
| LIMA eigenvector centrality (scRNAseq) | 24.000 |
| CellOracle degree centrality (scRNAseq) | 155.000 |
| CellOracle out centrality (scRNAseq) | 157.000 |
| CellOracle in centrality (scRNAseq) | 40.000 |
| CellOracle betweenness centrality (scRNAseq) | 46.000 |
| CellOracle eigenvector centrality (scRNAseq) | 72.000 |
| CellOracle degree centrality (GeneSys) | 136.000 |
| CellOracle out centrality (GeneSys) | 151.000 |
| CellOracle in centrality (GeneSys) | 27.000 |
| CellOracle betweenness centrality (GeneSys) | 34.000 |
| CellOracle eigenvector centrality (GeneSys) | 123.000 |
| DE myAUC rank (scRNAseq) | 102.000 |
| DE avg diff rank (scRNAseq) | 108.000 |
| DE myAUC rank (GeneSys) | 78.000 |
| DE avg diff rank (GeneSys) | 90.000 |
| Permutation | 742.959 |
In [139]:
options(repr.plot.width=10, repr.plot.height=8)
ggplot(toplt, aes(x=reorder(Methods, R50, decreasing = TRUE), y=R50)) + geom_point(size=4)+
labs(title="Xylem-specific TF Prioritization Performance",x="", y = "R50")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [140]:
toplt <- data.frame(Methods=factor(c(rep("LIMA (GeneSys)",5), rep("LIMA (scRNAseq)",5), rep("CellOracle (scRNAseq)",5), rep("CellOracle (GeneSys)",5), rep("DE (scRNAseq)",2), rep("DE (GeneSys)",2), "Permutation"),
levels=c("Permutation", "CellOracle (scRNAseq)", "DE (scRNAseq)", "LIMA (scRNAseq)", "CellOracle (GeneSys)", "DE (GeneSys)", "LIMA (GeneSys)")), R50=toplt$R50)
options(repr.plot.width=6, repr.plot.height=5)
ggplot(toplt, aes(x=Methods, y=R50))+
geom_boxplot(width=0.4)+
labs(title="Xylem-specific TF Prioritization Performance",x="", y = "R50")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
Trichoblast¶
In [141]:
gsgo <- read.csv("./Gold_Standard_Root_TF_StringDB.csv", header=TRUE)
gsgo <- gsgo[which(gsgo[,8]=="Yes"),]
gsgo$preferredName
gsgo <- gsgo$GeneID
gsgo <- intersect(gsgo, gsgo_miniex_stringdb)
- 'AL6'
- 'ANL2'
- 'BHLH12'
- 'BHLH2'
- 'BHLH32'
- 'BHLH54'
- 'BHLH66'
- 'BHLH69'
- 'BHLH82'
- 'BHLH83'
- 'BHLH85'
- 'BHLH86'
- 'CPC'
- 'E2FB'
- 'ETC1'
- 'GALT2'
- 'GIS3'
- 'GL2'
- 'GL3'
- 'GTL1'
- 'HDG11'
- 'HDG12'
- 'HDG2'
- 'MAMYB'
- 'MYB23'
- 'MYB86'
- 'RBR1'
- 'SCL22'
- 'SCL27'
- 'SCL6'
- 'TRY'
- 'TTG1'
- 'WER'
- 'ZFP5'
- 'ZFP6'
- 'ZFP8'
In [142]:
length(gsgo)
r50 <- 5
34
In [143]:
## Define genesys
run_r50_genesys <- function(x){
genesys$ct_score <- min_max_normalize(rowSums(apply(genesys[,grep(paste0('^tri_',x),colnames(genesys))],2,as.numeric)))
genesys$combined_score <- genesys$ct_score
genesys <- genesys %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(genesys))){
if (genesys$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [144]:
## Define raw
run_r50_scRNAseq <- function(x){
raw$ct_score <- min_max_normalize(rowSums(apply(raw[,grep(paste0('^tri_',x),colnames(raw))],2,as.numeric)))
raw$combined_score <- raw$ct_score
raw <- raw %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(raw))){
if (raw$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [145]:
## Define celloracle
tri <- prepros("../celloracle/trichoblast_X_root_integrated_celloracle_gene_score_iGRN.csv")
## Define celloracle
dat <- rbind(tri)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all),
in_centrality = min_max_normalize(degree_centrality_in),
out_centrality = min_max_normalize(degree_centrality_out),
betweenness_centrality = min_max_normalize(betweenness_centrality),
eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality),
in_centrality = sum(in_centrality),
out_centrality = sum(out_centrality),
betweenness_centrality = sum(betweenness_centrality),
eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:6],2,min_max_normalize))
celloracle_scRNAseq <- dat
## Define celloracle
tri <- prepros("../celloracle/trichoblast_P_root_integrated_celloracle_gene_score_iGRN.csv")
## Define celloracle
dat <- rbind(tri)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all),
in_centrality = min_max_normalize(degree_centrality_in),
out_centrality = min_max_normalize(degree_centrality_out),
betweenness_centrality = min_max_normalize(betweenness_centrality),
eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality),
in_centrality = sum(in_centrality),
out_centrality = sum(out_centrality),
betweenness_centrality = sum(betweenness_centrality),
eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:6],2,min_max_normalize))
celloracle_GeneSys <- dat
run_r50_celloracle <- function(x, dat){
celloracle <- dat
celloracle$combined_score <- celloracle[,grep(x,colnames(celloracle))]
celloracle <- celloracle %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(celloracle))){
if (celloracle$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [146]:
## Define DE
de <- read.csv("Input_X_root_markers_ROC.csv")
de <- de[c(grep("Trichoblast",de$cluster)),]
de <- de[!is.na(match(de$gene,exptf)),]
dat <- de %>% group_by(gene) %>% reframe(myAUC_rank = mean(myAUC_rank),avg_diff_rank = mean(avg_diff_rank))
colnames(dat) <- c("GeneID","myAUC_rank","avg_diff_rank")
de_scRNAseq <- dat
de <- read.csv("GeneSys_P_root_markers_ROC.csv")
de <- de[c(grep("Trichoblast",de$cluster)),]
de <- de[!is.na(match(de$gene,exptf)),]
dat <- de %>% group_by(gene) %>% reframe(myAUC_rank = mean(myAUC_rank),avg_diff_rank = mean(avg_diff_rank))
colnames(dat) <- c("GeneID","myAUC_rank","avg_diff_rank")
de_GeneSys <- dat
run_r50_de <- function(x, dat){
de <- dat
de$combined_score <- de[,grep(x,colnames(de))]
de <- de %>% arrange(desc(combined_score))
count <- 0
for (i in seq(nrow(de))){
if (de$GeneID[i] %in% gsgo){
count <- count +1
if (count == r50){
return(i)
break
}
}
}
}
In [147]:
length(intersect(de_GeneSys$GeneID, gsgo))
6
In [148]:
## Expressed TFs permutation
R50_permutation <- c()
for (j in 1:1000){
ran <- sample(exptf, length(exptf), replace=FALSE)
count <- 0
for (i in seq(length(ran))){
if (ran[i] %in% gsgo){
count <- count +1
if (count == r50){
R50_permutation <- c(R50_permutation,i)
break
}
}
}
}
In [149]:
toplt <- data.frame(Methods=c("LIMA degree centrality (GeneSys)", "LIMA out centrality (GeneSys)", "LIMA in centrality (GeneSys)", "LIMA betweenness centrality (GeneSys)",
"LIMA eigenvector centrality (GeneSys)","LIMA degree centrality (scRNAseq)", "LIMA out centrality (scRNAseq)", "LIMA in centrality (scRNAseq)", "LIMA betweenness centrality (scRNAseq)",
"LIMA eigenvector centrality (scRNAseq)", "CellOracle degree centrality (scRNAseq)", "CellOracle out centrality (scRNAseq)",
"CellOracle in centrality (scRNAseq)", "CellOracle betweenness centrality (scRNAseq)", "CellOracle eigenvector centrality (scRNAseq)",
"CellOracle degree centrality (GeneSys)", "CellOracle out centrality (GeneSys)",
"CellOracle in centrality (GeneSys)", "CellOracle betweenness centrality (GeneSys)", "CellOracle eigenvector centrality (GeneSys)",
"DE myAUC rank (scRNAseq)", "DE avg diff rank (scRNAseq)", "DE myAUC rank (GeneSys)", "DE avg diff rank (GeneSys)", "Permutation"),
R50=c(run_r50_genesys('degree_centrality'),run_r50_genesys('out_centrality'),run_r50_genesys('in_centrality'),
run_r50_genesys('betweenness_centrality'),run_r50_genesys('eigenvector_centrality'),
run_r50_scRNAseq('degree_centrality'),run_r50_scRNAseq('out_centrality'),run_r50_scRNAseq('in_centrality'),
run_r50_scRNAseq('betweenness_centrality'),run_r50_scRNAseq('eigenvector_centrality'),
run_r50_celloracle('degree_centrality', celloracle_scRNAseq),run_r50_celloracle('out_centrality', celloracle_scRNAseq),run_r50_celloracle('in_centrality', celloracle_scRNAseq),
run_r50_celloracle('betweenness_centrality', celloracle_scRNAseq),run_r50_celloracle('eigenvector_centrality', celloracle_scRNAseq),
run_r50_celloracle('degree_centrality', celloracle_GeneSys),run_r50_celloracle('out_centrality', celloracle_GeneSys),run_r50_celloracle('in_centrality', celloracle_GeneSys),
run_r50_celloracle('betweenness_centrality', celloracle_GeneSys),run_r50_celloracle('eigenvector_centrality', celloracle_GeneSys),
run_r50_de('myAUC_rank', de_scRNAseq),run_r50_de('avg_diff_rank', de_scRNAseq),run_r50_de('myAUC_rank', de_GeneSys),run_r50_de('avg_diff_rank', de_GeneSys), mean(R50_permutation)))
In [150]:
toplt
| Methods | R50 |
|---|---|
| <chr> | <dbl> |
| LIMA degree centrality (GeneSys) | 20.000 |
| LIMA out centrality (GeneSys) | 23.000 |
| LIMA in centrality (GeneSys) | 31.000 |
| LIMA betweenness centrality (GeneSys) | 19.000 |
| LIMA eigenvector centrality (GeneSys) | 20.000 |
| LIMA degree centrality (scRNAseq) | 20.000 |
| LIMA out centrality (scRNAseq) | 22.000 |
| LIMA in centrality (scRNAseq) | 26.000 |
| LIMA betweenness centrality (scRNAseq) | 16.000 |
| LIMA eigenvector centrality (scRNAseq) | 20.000 |
| CellOracle degree centrality (scRNAseq) | 17.000 |
| CellOracle out centrality (scRNAseq) | 151.000 |
| CellOracle in centrality (scRNAseq) | 19.000 |
| CellOracle betweenness centrality (scRNAseq) | 45.000 |
| CellOracle eigenvector centrality (scRNAseq) | 50.000 |
| CellOracle degree centrality (GeneSys) | 63.000 |
| CellOracle out centrality (GeneSys) | 133.000 |
| CellOracle in centrality (GeneSys) | 23.000 |
| CellOracle betweenness centrality (GeneSys) | 32.000 |
| CellOracle eigenvector centrality (GeneSys) | 95.000 |
| DE myAUC rank (scRNAseq) | 29.000 |
| DE avg diff rank (scRNAseq) | 27.000 |
| DE myAUC rank (GeneSys) | 25.000 |
| DE avg diff rank (GeneSys) | 22.000 |
| Permutation | 216.348 |
In [151]:
options(repr.plot.width=10, repr.plot.height=8)
ggplot(toplt, aes(x=reorder(Methods, R50, decreasing = TRUE), y=R50)) + geom_point(size=4)+
labs(title="Trichoblast-specific TF Prioritization Performance",x="", y = "R15")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [152]:
toplt <- data.frame(Methods=factor(c(rep("LIMA (GeneSys)",5), rep("LIMA (scRNAseq)",5), rep("CellOracle (scRNAseq)",5), rep("CellOracle (GeneSys)",5), rep("DE (scRNAseq)",2), rep("DE (GeneSys)",2), "Permutation"),
levels=c("Permutation", "CellOracle (scRNAseq)", "DE (scRNAseq)", "LIMA (scRNAseq)", "CellOracle (GeneSys)", "DE (GeneSys)", "LIMA (GeneSys)")), R50=toplt$R50)
options(repr.plot.width=6, repr.plot.height=5)
ggplot(toplt, aes(x=Methods, y=R50))+
geom_boxplot(width=0.4)+
labs(title="Trichoblast-specific TF Prioritization Performance",x="", y = "R15")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
Plotting¶
In [154]:
dat <- genesys
In [155]:
plot_heatmap <- function(gene, centrality){
sub <- as.numeric(t(dat[which(dat$TF==gene),grep(centrality,colnames(dat))]))
sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
## Cell types
rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
## Transition
colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
# Reorder rows
sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = TRUE, name = paste0(gene,"\n","weighted","\n","network","\n","centrality"),
col = col_fun, column_title = paste0(str_split_i(centrality,"_",1),"\n",str_split_i(centrality,"_",2)), column_title_gp = gpar(fontsize = 12, fontface = "bold"),
cluster_rows = FALSE, cluster_columns = FALSE,
show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
)
}
In [156]:
plot_heatmap2 <- function(gene, centrality){
sub <- as.numeric(t(dat[which(dat$TF==gene),grep(centrality,colnames(dat))]))
sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
## Cell types
rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
## Transition
colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
# Reorder rows
sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
col = col_fun, column_title = "out degree \n centrality", column_title_gp = gpar(fontsize = 12, fontface = "bold"),
cluster_rows = FALSE, cluster_columns = FALSE,
show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
)
}
In [157]:
plot_heatmap3 <- function(gene, centrality){
sub <- as.numeric(t(dat[which(dat$TF==gene),grep(centrality,colnames(dat))]))
sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
## Cell types
rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
## Transition
colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
# Reorder rows
sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1), name = paste0("unweighted","\n","network","\n","centrality"),
col = col_fun, column_title = "in degree \n centrality", column_title_gp = gpar(fontsize = 12, fontface = "bold"),
cluster_rows = FALSE, cluster_columns = FALSE,
show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
)
}
In [158]:
plot_all_centrality <- function(gene){
options(repr.plot.width=10, repr.plot.height=6)
plot_heatmap(gene,"betweenness_centrality") + plot_heatmap2(gene,"out_centrality") + plot_heatmap3(gene,"in_centrality")
}
In [159]:
plot_all_centrality("TTG1")
In [149]:
plot_all_centrality("CPC")
In [150]:
plot_all_centrality("SHR")
In [151]:
plot_all_centrality("SCR")
In [152]:
plot_all_centrality("BLJ")
In [153]:
plot_all_centrality("JKD")
In [154]:
plot_all_centrality("MYB36")
In [155]:
plot_all_centrality("RVN")
In [156]:
plot_all_centrality("MGP")
In [157]:
plot_all_centrality("NUC")
In [158]:
plot_all_centrality("WER")
In [159]:
## HAT7
plot_all_centrality("HAT7")
In [160]:
## GATA10
plot_all_centrality("GATA10")
In [161]:
## GATA11
plot_all_centrality("GATA11")
In [162]:
plot_all_centrality("AN3")
In [163]:
plot_all_centrality("GL2")
In [164]:
plot_all_centrality("LBD15")
In [165]:
plot_all_centrality <- function(gene){
options(repr.plot.width=8, repr.plot.height=4)
plot_heatmap(gene,"betweenness_centrality") + plot_heatmap2(gene,"out_centrality") + plot_heatmap3(gene,"in_centrality")
}
In [166]:
plot_all_centrality("SHR")
In [167]:
plot_all_centrality("WER")
Rank by betweenness centrality, out-degree centrality, in-degree centrality and degree centrality¶
In [160]:
plot_bc <- function(gene){
sub <- as.numeric(t(dat[which(dat$TF==gene),grep("betweenness_centrality",colnames(dat))]))
sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
## Cell types
rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
## Transition
colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
# Reorder rows
sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
col = col_fun, column_title = gene, column_title_gp = gpar(fontsize = 12, fontface = "bold"),
cluster_rows = FALSE, cluster_columns = FALSE,
show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
)
}
In [161]:
plot_oc <- function(gene){
sub <- as.numeric(t(dat[which(dat$TF==gene),grep("out_centrality",colnames(dat))]))
sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
## Cell types
rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
## Transition
colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
# Reorder rows
sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
col = col_fun, column_title = gene, column_title_gp = gpar(fontsize = 12, fontface = "bold"),
cluster_rows = FALSE, cluster_columns = FALSE,
show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
)
}
In [162]:
plot_ic <- function(gene){
sub <- as.numeric(t(dat[which(dat$TF==gene),grep("in_centrality",colnames(dat))]))
sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
## Cell types
rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
## Transition
colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
# Reorder rows
sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
col = col_fun, column_title = gene, column_title_gp = gpar(fontsize = 12, fontface = "bold"),
cluster_rows = FALSE, cluster_columns = FALSE,
show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
)
}
In [163]:
plot_dc <- function(gene){
sub <- as.numeric(t(dat[which(dat$TF==gene),grep("degree_centrality",colnames(dat))]))
sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
## Cell types
rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
## Transition
colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
# Reorder rows
sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
col = col_fun, column_title = gene, column_title_gp = gpar(fontsize = 12, fontface = "bold"),
cluster_rows = FALSE, cluster_columns = FALSE,
show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
)
}
In [164]:
plot_blank <- function(gene){
sub <- as.numeric(t(dat[which(dat$TF==gene),grep("betweenness_centrality",colnames(dat))]))
sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
## Cell types
rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
## Transition
colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
# Reorder rows
sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
col_fun = colorRamp2(c(0, 0.001, 1), c('white',"white", "white"))
Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
col = col_fun, column_title_gp = gpar(fontsize = 12, fontface = "bold"),
cluster_rows = FALSE, cluster_columns = FALSE,
show_column_names = FALSE, show_row_names = TRUE
)
}
In [165]:
# Combine the top three centralies
sub <- dat[,grep("betweenness_centrality|in_centrality|out_centrality",colnames(dat))]
sub <- as.data.frame(sapply(sub, as.numeric))
rownames(sub) <- dat$TF
In [166]:
head(sub)
| atri_out_centrality_1 | atri_in_centrality_1 | atri_betweenness_centrality_1 | tri_out_centrality_1 | tri_in_centrality_1 | tri_betweenness_centrality_1 | lrc_out_centrality_1 | lrc_in_centrality_1 | lrc_betweenness_centrality_1 | cor_out_centrality_1 | ... | pro_betweenness_centrality_5 | xyl_out_centrality_5 | xyl_in_centrality_5 | xyl_betweenness_centrality_5 | phl_out_centrality_5 | phl_in_centrality_5 | phl_betweenness_centrality_5 | col_out_centrality_5 | col_in_centrality_5 | col_betweenness_centrality_5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ... | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| BZIP9 | 0.0000000 | 0.00000 | 0.0000000 | 0.0000000 | 0.000000000 | 0.0000000 | 0.0000000 | 0.000000 | 0.0000000 | 0.0000000 | ... | 0.9492408 | 0.00000000 | 0.0000000 | 0.000000000 | 0.93623639 | 0.05433186 | 0.9271092 | 0 | 0 | 0 |
| AT3G43430 | 0.0000000 | 0.00000 | 0.0000000 | 0.0000000 | 0.000000000 | 0.0000000 | 0.0000000 | 0.000000 | 0.0000000 | 0.0000000 | ... | 1.0000000 | 0.01276596 | 0.1307692 | 0.003010817 | 0.11975117 | 0.09544787 | 0.9688674 | 0 | 0 | 0 |
| GATA2 | 0.9953052 | 0.55706 | 0.5787153 | 0.9610390 | 0.386831276 | 0.9894677 | 0.1764706 | 0.496124 | 0.9910266 | 0.8266254 | ... | 0.0000000 | 0.00000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.0000000 | 0 | 0 | 0 |
| LEP | 0.0000000 | 0.00000 | 0.0000000 | 0.1558442 | 0.008230453 | 0.0000000 | 0.0000000 | 0.000000 | 0.0000000 | 0.0000000 | ... | 0.0000000 | 0.00000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.0000000 | 0 | 0 | 0 |
| MYB20 | 0.0000000 | 0.00000 | 0.0000000 | 0.0000000 | 0.000000000 | 0.0000000 | 0.0000000 | 0.000000 | 0.0000000 | 0.0000000 | ... | 0.9885568 | 0.05106383 | 0.1346154 | 0.000000000 | 0.00155521 | 0.01027900 | 0.0000000 | 0 | 0 | 0 |
| OBP2 | 0.0000000 | 0.00000 | 0.0000000 | 0.0000000 | 0.000000000 | 0.0000000 | 0.0000000 | 0.000000 | 0.0000000 | 0.0000000 | ... | 0.1289153 | 0.00000000 | 0.0000000 | 0.000000000 | 0.87713841 | 0.41703377 | 0.4722289 | 0 | 0 | 0 |
In [167]:
bc_rank <- data.frame(all=rowSums(sub),atri=rowSums(sub[,grep("^atri_",colnames(sub))]),tri=rowSums(sub[,grep("^tri_",colnames(sub))])
,cor=rowSums(sub[,grep("^cor_",colnames(sub))]),end=rowSums(sub[,grep("^end_",colnames(sub))])
,per=rowSums(sub[,grep("^per_",colnames(sub))]),pro=rowSums(sub[,grep("^pro_",colnames(sub))])
,xyl=rowSums(sub[,grep("^xyl_",colnames(sub))]),phl=rowSums(sub[,grep("^phl_",colnames(sub))])
,lrc=rowSums(sub[,grep("^lrc_",colnames(sub))]),col=rowSums(sub[,grep("^col_",colnames(sub))]))
In [168]:
bc_rank$GeneID <- wanted_TFs$GeneID[match(rownames(bc_rank),wanted_TFs$Name)]
In [169]:
head(bc_rank)
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | |
| BZIP9 | 29.19444 | 0.000000 | 0.000000 | 0.000000 | 0.01983748 | 9.05092129 | 8.8677625 | 0.05687082 | 11.19905212 | 0.00000 | 0.00000000 | AT5G24800 |
| AT3G43430 | 34.91987 | 0.000000 | 0.000000 | 0.000000 | 0.01684128 | 12.12719380 | 9.5079047 | 4.47480476 | 8.79312886 | 0.00000 | 0.00000000 | AT3G43430 |
| GATA2 | 32.60274 | 6.876734 | 5.844258 | 1.938035 | 2.47836267 | 0.03634172 | 0.1234438 | 0.02555554 | 0.02737594 | 11.82659 | 3.42603973 | AT2G45050 |
| LEP | 34.89938 | 4.738138 | 5.608289 | 0.000000 | 5.48517444 | 11.78585516 | 4.1985151 | 0.14260467 | 2.92113981 | 0.00000 | 0.01966089 | AT5G13910 |
| MYB20 | 30.03240 | 0.000000 | 0.000000 | 0.000000 | 0.31968431 | 10.63089110 | 10.9812467 | 4.08713837 | 4.01344128 | 0.00000 | 0.00000000 | AT1G66230 |
| OBP2 | 20.54627 | 0.000000 | 0.000000 | 0.000000 | 0.00000000 | 5.48240701 | 5.4416662 | 0.00000000 | 9.62219734 | 0.00000 | 0.00000000 | AT1G07640 |
Atrichoblast¶
In [178]:
atri_rank <- bc_rank[which(bc_rank$atri*2 > bc_rank$all),]%>% arrange(desc(atri))
atri_rank$GeneName <- rownames(atri_rank)
In [179]:
atri_rank
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | |
| GL2 | 10.13792179 | 7.99892003 | 0.306455175 | 1.647865277 | 0.081783175 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.102898124 | 0.000000000 | AT1G79840 | GL2 |
| TTG2 | 7.89124644 | 7.64671499 | 0.154310924 | 0.063545673 | 0.026674853 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G37260 | TTG2 |
| MYB23 | 11.76429265 | 7.42441618 | 2.547943239 | 1.689051869 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.090423029 | 0.012458333 | AT5G40330 | MYB23 |
| MYB45 | 8.44734650 | 4.93461168 | 0.257495007 | 3.255239816 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT3G48920 | MYB45 |
| TRY | 9.29644062 | 4.84571448 | 0.306914408 | 1.281555407 | 0.944359511 | 0.005098190 | 0.000000000 | 0.000000000 | 0.047787381 | 0.172898617 | 1.692112629 | AT5G53200 | TRY |
| NAC6 | 5.01368983 | 4.61200210 | 0.250083668 | 0.089754727 | 0.025425394 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.009141583 | 0.027282363 | AT5G39610 | NAC6 |
| WRKY45 | 6.49790603 | 4.35144216 | 0.635707123 | 0.119099988 | 0.000000000 | 0.000000000 | 0.199622680 | 0.072935273 | 0.454857483 | 0.059754738 | 0.604486579 | AT3G01970 | WRKY45 |
| AT3G05860 | 4.55350580 | 3.80864079 | 0.744865009 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT3G05860 | AT3G05860 |
| HB17 | 6.33928305 | 3.80085860 | 0.563948902 | 0.116204080 | 0.000000000 | 0.000000000 | 0.048250157 | 1.579969548 | 0.147820723 | 0.000000000 | 0.082231041 | AT2G01430 | HB17 |
| AT2G28710 | 3.81928110 | 3.43981761 | 0.293076739 | 0.032832437 | 0.010034500 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.043519813 | AT2G28710 | AT2G28710 |
| FIT | 4.01278871 | 2.95155372 | 0.748342298 | 0.000000000 | 0.017182364 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.186714222 | 0.108996107 | AT2G28160 | FIT |
| MC2 | 3.08300059 | 2.83101418 | 0.251986404 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT4G25110 | MC2 |
| BNQ3 | 3.97464433 | 2.40453936 | 0.066772451 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 1.503332517 | 0.000000000 | AT3G47710 | BNQ3 |
| WRKY61 | 4.32682753 | 2.25471857 | 1.978823935 | 0.093285030 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G18860 | WRKY61 |
| HB24 | 2.62056444 | 2.23005005 | 0.106957257 | 0.033819143 | 0.000000000 | 0.000000000 | 0.230394668 | 0.019343320 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G18350 | HB24 |
| MEA | 2.18297879 | 2.17727893 | 0.005699856 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G02580 | MEA |
| OFP18 | 1.86178389 | 1.83703642 | 0.024747475 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT3G52540 | OFP18 |
| DAR4 | 2.98895379 | 1.75552365 | 0.053711720 | 0.053010104 | 0.402188741 | 0.246628483 | 0.237168173 | 0.000000000 | 0.000000000 | 0.074360779 | 0.166362147 | AT5G17890 | DAR4 |
| AT5G22890 | 2.16356429 | 1.60654355 | 0.320972703 | 0.122956829 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.018729097 | 0.094362106 | AT5G22890 | AT5G22890 |
| AT1G14600 | 1.45340795 | 1.43902872 | 0.000000000 | 0.000000000 | 0.014379231 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G14600 | AT1G14600 |
| RMR1 | 1.63610258 | 1.28056690 | 0.036346883 | 0.000000000 | 0.057576953 | 0.068553430 | 0.085902648 | 0.081887938 | 0.025267827 | 0.000000000 | 0.000000000 | AT5G66160 | RMR1 |
| WRKY13 | 1.33031602 | 1.26334815 | 0.000000000 | 0.066967867 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT4G39410 | WRKY13 |
| WRKY47 | 1.57969011 | 0.98789256 | 0.269416944 | 0.144009266 | 0.178371343 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT4G01720 | WRKY47 |
| AT3G07260 | 1.20787688 | 0.92078962 | 0.000000000 | 0.073675348 | 0.113733873 | 0.011136881 | 0.000000000 | 0.000000000 | 0.000000000 | 0.011019912 | 0.077521244 | AT3G07260 | AT3G07260 |
| AT2G18670 | 1.64449246 | 0.85916630 | 0.320715329 | 0.229466562 | 0.014339778 | 0.000000000 | 0.000000000 | 0.000000000 | 0.008172064 | 0.085456013 | 0.127176412 | AT2G18670 | AT2G18670 |
| NF-YC12 | 1.24759224 | 0.77742287 | 0.000000000 | 0.004667502 | 0.000000000 | 0.012731777 | 0.000000000 | 0.089802004 | 0.000000000 | 0.105516622 | 0.257451465 | AT5G38140 | NF-YC12 |
| AT3G13840 | 0.76429932 | 0.72259803 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.041701293 | AT3G13840 | AT3G13840 |
| DDL | 1.17790388 | 0.62444734 | 0.067408789 | 0.051432801 | 0.101113681 | 0.079340660 | 0.073521447 | 0.000000000 | 0.012539131 | 0.034125428 | 0.133974606 | AT3G20550 | DDL |
| PIE1 | 1.00612879 | 0.56826933 | 0.000000000 | 0.000000000 | 0.056384220 | 0.095642719 | 0.099555085 | 0.005759741 | 0.029067524 | 0.039489077 | 0.111961095 | AT3G12810 | PIE1 |
| AT4G31650 | 0.68221005 | 0.45461762 | 0.227592428 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT4G31650 | AT4G31650 |
| NAC069 | 0.84350278 | 0.44204593 | 0.086504488 | 0.000000000 | 0.005038830 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.135040823 | 0.174872710 | AT4G01550 | NAC069 |
| AT4G01350 | 0.75559514 | 0.40966136 | 0.116591161 | 0.040466025 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.188876593 | 0.000000000 | AT4G01350 | AT4G01350 |
| NLP4 | 0.50917868 | 0.39472582 | 0.000000000 | 0.027496935 | 0.000000000 | 0.000000000 | 0.017543661 | 0.000000000 | 0.058858251 | 0.000000000 | 0.010554010 | AT1G20640 | NLP4 |
| PHE1 | 0.56448116 | 0.34767753 | 0.216803631 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G65330 | PHE1 |
| HSFB3 | 0.53739224 | 0.34211328 | 0.000000000 | 0.177288392 | 0.000000000 | 0.012976906 | 0.005013669 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G41690 | HSFB3 |
| AT2G19260 | 0.53244748 | 0.28769896 | 0.058040725 | 0.000000000 | 0.008229287 | 0.020079140 | 0.051042715 | 0.066911234 | 0.027224574 | 0.000000000 | 0.013220848 | AT2G19260 | AT2G19260 |
| ULT2 | 0.39597108 | 0.21941519 | 0.000000000 | 0.000000000 | 0.176555891 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G20825 | ULT2 |
| RRS1 | 0.28433684 | 0.17275753 | 0.035374882 | 0.000000000 | 0.027835807 | 0.000000000 | 0.000000000 | 0.026968733 | 0.011562476 | 0.003772636 | 0.006064778 | AT5G45260 | RRS1 |
| AT5G07400 | 0.26731930 | 0.16676403 | 0.000000000 | 0.009829982 | 0.013414992 | 0.046455921 | 0.012555782 | 0.000000000 | 0.000000000 | 0.006001577 | 0.012297022 | AT5G07400 | AT5G07400 |
| LBD26 | 0.11595837 | 0.08841932 | 0.020684704 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.006854354 | AT3G27940 | LBD26 |
| AT4G18110 | 0.08110203 | 0.07035167 | 0.010750361 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT4G18110 | AT4G18110 |
| AT4G38070 | 0.05551053 | 0.03312664 | 0.000000000 | 0.000000000 | 0.015014666 | 0.000000000 | 0.007369229 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT4G38070 | AT4G38070 |
| AT4G14225 | 0.04358049 | 0.02828557 | 0.011791627 | 0.000000000 | 0.000000000 | 0.003503293 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT4G14225 | AT4G14225 |
| PRR3 | 0.03702299 | 0.02638461 | 0.000000000 | 0.000000000 | 0.000000000 | 0.010638372 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G60100 | PRR3 |
In [180]:
options(repr.plot.width=6, repr.plot.height=4)
ggplot(atri_rank[1:10,], aes(x=reorder(GeneName, atri, decreasing = FALSE), y=atri)) + geom_point(size=4)+
labs(title="Atrichoblast-specific TF Prioritization",x="", y = "Combined centrality score")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [181]:
write.csv(atri_rank,"Atrichoblast_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)
In [182]:
tf_rank <- atri_rank %>% rownames(.)
In [183]:
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])
In [184]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [185]:
options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Atrichoblast ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
In [186]:
q1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3])
q2 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3])
q3 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3])
q1 <- grid.grabExpr(draw(q1))
q2 <- grid.grabExpr(draw(q2))
q3 <- grid.grabExpr(draw(q3))
options(repr.plot.width=6, repr.plot.height=8)
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q1,nrow=2,rel_heights = c(1, 8))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q2,nrow=2,rel_heights = c(1, 8))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q3,nrow=2,rel_heights = c(1, 8)),nrow=3)
Trichoblast¶
In [187]:
tri_rank <- bc_rank[which(bc_rank$tri*2 > bc_rank$all),]%>% arrange(desc(tri))
tri_rank$GeneName <- rownames(tri_rank)
In [188]:
tri_rank
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | |
| AT3G53370 | 9.48156264 | 0.630396168 | 8.27226699 | 0.000000000 | 0.000000000 | 0.264379307 | 0.032137564 | 0.260165608 | 0.022217008 | 0.000000000 | 0.000000000 | AT3G53370 | AT3G53370 |
| LRL3 | 7.55971931 | 0.328339547 | 7.23137977 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G58010 | LRL3 |
| RHD6 | 7.62172822 | 0.599061357 | 7.00936422 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.013302640 | 0.000000000 | 0.000000000 | AT1G66470 | RHD6 |
| AT4G09100 | 6.15424262 | 0.019034250 | 6.13520837 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT4G09100 | AT4G09100 |
| WRKY65 | 11.09069591 | 2.627264362 | 6.09064583 | 0.068166275 | 0.292756193 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 1.358809122 | 0.653054119 | AT1G29280 | WRKY65 |
| ATMYC1 | 9.91207908 | 2.531639789 | 6.05062215 | 1.042949013 | 0.227053423 | 0.059814700 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT4G00480 | ATMYC1 |
| RSL4 | 5.56741048 | 0.066248152 | 5.50116232 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G27740 | RSL4 |
| RSL1 | 7.20896486 | 1.092990071 | 5.09793148 | 0.000000000 | 1.018043312 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G37800 | RSL1 |
| OFP13 | 6.20947806 | 0.595148296 | 4.78905453 | 0.649866798 | 0.097502013 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.077906431 | 0.000000000 | AT5G04820 | OFP13 |
| RSL2 | 4.07352837 | 0.000000000 | 4.07352837 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT4G33880 | RSL2 |
| ZF1 | 4.93116304 | 0.112995409 | 3.70990372 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.461863631 | 0.000000000 | 0.646400286 | 0.000000000 | AT5G67450 | ZF1 |
| ESE3 | 4.65871851 | 0.088612387 | 3.60025906 | 0.969847064 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G25190 | ESE3 |
| AGL87 | 3.40158316 | 0.788902283 | 2.61268088 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G22590 | AGL87 |
| AT5G06800 | 2.94696696 | 0.335803636 | 2.60101730 | 0.007387144 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.002758878 | 0.000000000 | 0.000000000 | AT5G06800 | AT5G06800 |
| MYB47 | 2.59538395 | 0.219799793 | 2.09473966 | 0.000000000 | 0.017042835 | 0.000000000 | 0.000000000 | 0.000000000 | 0.014631514 | 0.041880303 | 0.207289836 | AT1G18710 | MYB47 |
| AT1G11490 | 3.28003613 | 1.427652071 | 1.85238406 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G11490 | AT1G11490 |
| AT5G56200 | 1.81396382 | 0.000000000 | 1.81396382 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G56200 | AT5G56200 |
| RL6 | 1.88163720 | 0.073055661 | 1.66087567 | 0.000000000 | 0.000000000 | 0.000000000 | 0.032740121 | 0.000000000 | 0.051414927 | 0.063550822 | 0.000000000 | AT1G75250 | RL6 |
| AT4G39160 | 1.96222524 | 0.139401430 | 1.64674013 | 0.000000000 | 0.013078915 | 0.040606978 | 0.021189234 | 0.007625412 | 0.021284906 | 0.012826962 | 0.059471269 | AT4G39160 | AT4G39160 |
| AT5G04390 | 1.78207216 | 0.000000000 | 1.37681521 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.405256948 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G04390 | AT5G04390 |
| RAP2.11 | 1.36564606 | 0.000000000 | 1.34858835 | 0.000000000 | 0.004381224 | 0.000000000 | 0.000000000 | 0.012676482 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G19790 | RAP2.11 |
| AT1G79220 | 2.11178858 | 0.105627792 | 1.32845814 | 0.352466022 | 0.107154700 | 0.117367305 | 0.030168455 | 0.054267204 | 0.016278961 | 0.000000000 | 0.000000000 | AT1G79220 | AT1G79220 |
| MBD4 | 2.04295166 | 0.305537328 | 1.32559498 | 0.000000000 | 0.011241491 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.161215636 | 0.239362226 | AT3G63030 | MBD4 |
| AT1G02040 | 1.33383176 | 0.000000000 | 1.28110478 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.052726984 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G02040 | AT1G02040 |
| AT3G55080 | 2.37892070 | 0.308658144 | 1.26494244 | 0.356005251 | 0.199936326 | 0.113757141 | 0.007369229 | 0.007788138 | 0.000000000 | 0.097755652 | 0.022708379 | AT3G55080 | AT3G55080 |
| AT2G05160 | 1.21184708 | 0.016530039 | 1.19531704 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G05160 | AT2G05160 |
| TCP24 | 1.16885985 | 0.000000000 | 1.11918348 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.049676370 | 0.000000000 | 0.000000000 | AT1G30210 | TCP24 |
| AT5G07900 | 1.99429345 | 0.204960547 | 1.01847103 | 0.108412564 | 0.041441155 | 0.036151604 | 0.107536496 | 0.308989155 | 0.139305674 | 0.029025231 | 0.000000000 | AT5G07900 | AT5G07900 |
| MED6 | 1.59879282 | 0.247883781 | 0.85418152 | 0.025580886 | 0.194777354 | 0.078517326 | 0.053866160 | 0.083047631 | 0.033944194 | 0.026993970 | 0.000000000 | AT3G21350 | MED6 |
| GL3 | 0.94686615 | 0.234044135 | 0.59190484 | 0.082121525 | 0.038795652 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G41315 | GL3 |
| LOL2 | 1.08562476 | 0.168264942 | 0.58896457 | 0.082934609 | 0.076711996 | 0.027083036 | 0.000000000 | 0.012900021 | 0.012521215 | 0.079378507 | 0.036865854 | AT4G21610 | LOL2 |
| RAD54 | 1.13349249 | 0.103271240 | 0.57204437 | 0.000000000 | 0.000000000 | 0.020706259 | 0.080859455 | 0.226245799 | 0.000000000 | 0.071498879 | 0.058866488 | AT3G19210 | RAD54 |
| SUVR4 | 0.60029598 | 0.049935779 | 0.49062269 | 0.004667502 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.009557344 | 0.045512671 | AT3G04380 | SUVR4 |
| PRR9 | 0.53234754 | 0.012844961 | 0.37374907 | 0.000000000 | 0.014172914 | 0.000000000 | 0.023804121 | 0.016202946 | 0.006394282 | 0.032444668 | 0.052734578 | AT2G46790 | PRR9 |
| AT2G17600 | 0.42308768 | 0.000000000 | 0.32460755 | 0.098480128 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G17600 | AT2G17600 |
| NAC005 | 0.32196148 | 0.015432099 | 0.30652938 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G02250 | NAC005 |
| AT3G51470 | 0.45504611 | 0.109568188 | 0.28143674 | 0.000000000 | 0.000000000 | 0.003503293 | 0.022323792 | 0.038214102 | 0.000000000 | 0.000000000 | 0.000000000 | AT3G51470 | AT3G51470 |
| AT1G61960 | 0.30299786 | 0.008563308 | 0.27501669 | 0.000000000 | 0.000000000 | 0.008299191 | 0.011118675 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G61960 | AT1G61960 |
| EIL2 | 0.26531972 | 0.000000000 | 0.26531972 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G21120 | EIL2 |
| EMB1789 | 0.38418730 | 0.008516190 | 0.25437631 | 0.000000000 | 0.031438113 | 0.012977554 | 0.021153623 | 0.044632416 | 0.011093098 | 0.000000000 | 0.000000000 | AT5G56930 | EMB1789 |
| AT2G01060 | 0.49076277 | 0.038859655 | 0.24942268 | 0.031249993 | 0.041245641 | 0.020387347 | 0.000000000 | 0.009034042 | 0.025152012 | 0.013078471 | 0.062332929 | AT2G01060 | AT2G01060 |
| LBD23 | 0.24496300 | 0.000000000 | 0.24496300 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT3G26620 | LBD23 |
| BPC5 | 0.27697606 | 0.021325098 | 0.23370911 | 0.000000000 | 0.000000000 | 0.007006587 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.014935261 | AT4G38910 | BPC5 |
| AT2G20030 | 0.19815926 | 0.000000000 | 0.19815926 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G20030 | AT2G20030 |
| AT2G24830 | 0.20159881 | 0.000000000 | 0.12405863 | 0.000000000 | 0.035775764 | 0.005870489 | 0.000000000 | 0.007745382 | 0.000000000 | 0.000000000 | 0.028148549 | AT2G24830 | AT2G24830 |
| AT5G06420 | 0.21327839 | 0.036153280 | 0.11994740 | 0.007510366 | 0.012746842 | 0.010638372 | 0.000000000 | 0.026282129 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G06420 | AT5G06420 |
| ASG3 | 0.20356297 | 0.000000000 | 0.11560462 | 0.000000000 | 0.003027545 | 0.044261048 | 0.017439787 | 0.005759741 | 0.017470231 | 0.000000000 | 0.000000000 | AT2G44980 | ASG3 |
| SUVH2 | 0.13692878 | 0.000000000 | 0.10800136 | 0.000000000 | 0.006294638 | 0.004706377 | 0.000000000 | 0.000000000 | 0.017926407 | 0.000000000 | 0.000000000 | AT2G33290 | SUVH2 |
| AT4G12850 | 0.15529158 | 0.010376193 | 0.08708876 | 0.000000000 | 0.007659913 | 0.000000000 | 0.000000000 | 0.029449608 | 0.020717105 | 0.000000000 | 0.000000000 | AT4G12850 | AT4G12850 |
| LDL2 | 0.11215934 | 0.000000000 | 0.07047664 | 0.000000000 | 0.004043805 | 0.000000000 | 0.000000000 | 0.037638885 | 0.000000000 | 0.000000000 | 0.000000000 | AT3G13682 | LDL2 |
| AT2G14760 | 0.05309808 | 0.000000000 | 0.05309808 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G14760 | AT2G14760 |
| IAA34 | 0.05840362 | 0.000000000 | 0.04961705 | 0.000000000 | 0.000000000 | 0.000000000 | 0.008786574 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G15050 | IAA34 |
| AT5G45113 | 0.04422799 | 0.000000000 | 0.04422799 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G45113 | AT5G45113 |
| BBX17 | 0.04720424 | 0.000000000 | 0.03787858 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.009325654 | AT1G49130 | BBX17 |
| LBD21 | 0.02532468 | 0.000000000 | 0.02532468 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT3G11090 | LBD21 |
| ZFP8 | 0.01758873 | 0.000000000 | 0.01758873 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G41940 | ZFP8 |
| IAA32 | 0.01947380 | 0.000000000 | 0.01218648 | 0.000000000 | 0.000000000 | 0.000000000 | 0.007287324 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G01200 | IAA32 |
In [189]:
options(repr.plot.width=6, repr.plot.height=4)
ggplot(tri_rank[1:10,], aes(x=reorder(GeneName, tri, decreasing = FALSE), y=tri)) + geom_point(size=4)+
labs(title="Trichoblast-specific TF Prioritization",x="", y = "Combined centrality score")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [190]:
write.csv(tri_rank,"Trichoblast_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)
In [191]:
## Top20 only
tf_rank <- tri_rank %>% rownames(.)
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])
In [192]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [193]:
options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Trichoblast ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
In [194]:
q1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3])
q2 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3])
q3 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3])
q1 <- grid.grabExpr(draw(q1))
q2 <- grid.grabExpr(draw(q2))
q3 <- grid.grabExpr(draw(q3))
options(repr.plot.width=6, repr.plot.height=8)
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q1,nrow=2,rel_heights = c(1, 8))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q2,nrow=2,rel_heights = c(1, 8))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q3,nrow=2,rel_heights = c(1, 8)),nrow=3)
Cortex¶
In [195]:
cor_rank <- bc_rank[which(bc_rank$cor*2 > bc_rank$all),]%>% arrange(desc(cor))
cor_rank$GeneName <- rownames(cor_rank)
In [196]:
cor_rank
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | |
| MYB86 | 8.33162471 | 2.75419560 | 0.253538113 | 5.20815202 | 0.007387534 | 0.000000000 | 0.000000000 | 0.07504240 | 0.020377054 | 0.012931996 | 0.000000000 | AT5G26660 | MYB86 |
| AT2G38300 | 4.48282427 | 0.22006411 | 0.000000000 | 4.26276016 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G38300 | AT2G38300 |
| AT1G72210 | 5.53571023 | 0.27108148 | 0.005699856 | 3.99582057 | 0.990212139 | 0.000000000 | 0.113503819 | 0.00000000 | 0.126564031 | 0.006243032 | 0.026585297 | AT1G72210 | AT1G72210 |
| AT2G42660 | 4.45027123 | 0.65196470 | 0.000000000 | 3.79830653 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G42660 | AT2G42660 |
| AT3G61420 | 5.05666724 | 0.40592662 | 0.114121032 | 3.70908262 | 0.636415577 | 0.004706377 | 0.096318075 | 0.00000000 | 0.058804976 | 0.031291966 | 0.000000000 | AT3G61420 | AT3G61420 |
| tny | 5.48007594 | 0.13479399 | 1.769093331 | 3.56176681 | 0.000000000 | 0.000000000 | 0.008461312 | 0.00000000 | 0.005960496 | 0.000000000 | 0.000000000 | AT5G25810 | tny |
| AT1G05710 | 5.61580676 | 0.13633733 | 0.007878508 | 3.40979001 | 1.270550511 | 0.020123959 | 0.105324581 | 0.00000000 | 0.595664361 | 0.023634337 | 0.046503165 | AT1G05710 | AT1G05710 |
| JKD | 6.26429609 | 0.35457101 | 0.000000000 | 3.20188027 | 2.698635654 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.009209155 | AT5G03150 | JKD |
| RGL3 | 5.10373007 | 0.33089100 | 0.222763629 | 3.14644102 | 0.121086849 | 0.044888418 | 0.031148307 | 0.38701667 | 0.000000000 | 0.501592428 | 0.317901759 | AT5G17490 | RGL3 |
| LAF1 | 4.57184963 | 0.89476535 | 0.011083636 | 3.05253630 | 0.603876820 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.009587514 | 0.000000000 | AT4G25560 | LAF1 |
| SIGF | 3.15478232 | 0.12334160 | 0.037892149 | 2.62497659 | 0.171834983 | 0.009174993 | 0.000000000 | 0.12719112 | 0.000000000 | 0.060370889 | 0.000000000 | AT2G36990 | SIGF |
| HAM3 | 4.07016308 | 0.49590950 | 0.063347763 | 2.47376946 | 0.229302520 | 0.024622544 | 0.072163020 | 0.00000000 | 0.032956665 | 0.421332802 | 0.256758806 | AT4G00150 | HAM3 |
| LRP1 | 4.30978967 | 0.06838522 | 0.000000000 | 2.46770932 | 0.344613700 | 0.788547822 | 0.451692451 | 0.00000000 | 0.188841157 | 0.000000000 | 0.000000000 | AT5G12330 | LRP1 |
| JAZ6 | 3.76880610 | 0.10155759 | 0.104990970 | 2.35973037 | 0.157072994 | 0.115616686 | 0.242138560 | 0.30208418 | 0.253398181 | 0.034002993 | 0.098213579 | AT1G72450 | JAZ6 |
| AT1G64380 | 4.03456987 | 1.49382898 | 0.040755538 | 2.02427183 | 0.431095617 | 0.000000000 | 0.028276829 | 0.00000000 | 0.010098052 | 0.006243032 | 0.000000000 | AT1G64380 | AT1G64380 |
| WRKY69 | 3.16964294 | 0.21777675 | 0.705542844 | 1.70080531 | 0.091870614 | 0.000000000 | 0.000000000 | 0.00000000 | 0.007074414 | 0.175513548 | 0.271059461 | AT3G58710 | WRKY69 |
| ETR2 | 2.30942829 | 0.32001716 | 0.000000000 | 1.61768421 | 0.168150722 | 0.000000000 | 0.040866634 | 0.03432003 | 0.019378350 | 0.019620959 | 0.089390226 | AT3G23150 | ETR2 |
| AT4G28030 | 2.74843264 | 0.79907560 | 0.419032001 | 1.41071946 | 0.107119522 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.012486065 | 0.000000000 | AT4G28030 | AT4G28030 |
| WRKY57 | 2.65614959 | 0.44239700 | 0.000000000 | 1.38151808 | 0.049825914 | 0.033043657 | 0.000000000 | 0.41720087 | 0.188634771 | 0.093153049 | 0.050376249 | AT1G69310 | WRKY57 |
| SIGE | 2.29579885 | 0.32977252 | 0.215778047 | 1.15253533 | 0.022988558 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.030178200 | 0.544546190 | AT5G24120 | SIGE |
| IDD4 | 1.35255595 | 0.00000000 | 0.000000000 | 1.12333103 | 0.229224922 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G02080 | IDD4 |
| GLK2 | 1.51880198 | 0.02983275 | 0.000000000 | 1.06184767 | 0.016972150 | 0.183153464 | 0.207474505 | 0.00000000 | 0.019521449 | 0.000000000 | 0.000000000 | AT5G44190 | GLK2 |
| OFP12 | 1.30271281 | 0.15446542 | 0.036258092 | 0.95511027 | 0.156879035 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G05420 | OFP12 |
| PIF7 | 0.80711754 | 0.00000000 | 0.049875152 | 0.51677908 | 0.002011285 | 0.000000000 | 0.000000000 | 0.00000000 | 0.238452028 | 0.000000000 | 0.000000000 | AT5G61270 | PIF7 |
| AT3G18960 | 0.65656493 | 0.12614226 | 0.000000000 | 0.44903381 | 0.024844620 | 0.000000000 | 0.056544242 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT3G18960 | AT3G18960 |
| AT2G46810 | 0.35062910 | 0.07499611 | 0.020487280 | 0.21886446 | 0.033491684 | 0.000000000 | 0.002789573 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G46810 | AT2G46810 |
| HB28 | 0.11929627 | 0.00000000 | 0.009090501 | 0.07065891 | 0.039546860 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT3G50890 | HB28 |
| PIL5 | 0.04446625 | 0.01049754 | 0.008893077 | 0.02507563 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G20180 | PIL5 |
In [197]:
options(repr.plot.width=6, repr.plot.height=4)
ggplot(cor_rank[1:10,], aes(x=reorder(GeneName, cor, decreasing = FALSE), y=cor)) + geom_point(size=4)+
labs(title="Cortex-specific TF Prioritization",x="", y = "Combined centrality score")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [198]:
write.csv(cor_rank,"Cortex_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)
In [199]:
tf_rank <- cor_rank %>% rownames(.)
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
#p4 <- plot_bc(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
#p8 <- plot_oc(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
#p12 <- plot_ic(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")
In [200]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
#p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
#p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
#p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [201]:
options(repr.plot.width=24, repr.plot.height=9)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Cortex ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,nrow=3),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,nrow=3),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,nrow=3),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
In [202]:
## Top 10 only
tf_rank <- cor_rank %>% rownames(.)
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
#p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
#p4 <- plot_bc(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
#p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
#p8 <- plot_oc(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
#p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
#p12 <- plot_ic(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")
In [203]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
#p3 <- grid.grabExpr(draw(p3))
#p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
#p7 <- grid.grabExpr(draw(p7))
#p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
#p11 <- grid.grabExpr(draw(p11))
#p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [204]:
options(repr.plot.width=24, repr.plot.height=5)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Cortex ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,nrow=2),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,nrow=2),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,nrow=2),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
In [205]:
q1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3])
q2 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3])
q3 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3])
q1 <- grid.grabExpr(draw(q1))
q2 <- grid.grabExpr(draw(q2))
q3 <- grid.grabExpr(draw(q3))
options(repr.plot.width=6, repr.plot.height=8)
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q1,nrow=2,rel_heights = c(1, 8))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q2,nrow=2,rel_heights = c(1, 8))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q3,nrow=2,rel_heights = c(1, 8)),nrow=3)
Endodermis¶
In [170]:
end_rank <- bc_rank[which(bc_rank$end*2 > bc_rank$all),]%>% arrange(desc(end))
end_rank$GeneName <- rownames(end_rank)
In [171]:
end_rank
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | |
| MYB36 | 11.465687275 | 0.00000000 | 0.000000000 | 0.000000000 | 11.295854022 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.16983325 | AT5G57620 | MYB36 |
| MYB74 | 10.873281001 | 0.00000000 | 0.000000000 | 0.067935848 | 8.801405251 | 0.000000000 | 0.138782683 | 0.341725875 | 0.101052072 | 0.211671939 | 1.21070733 | AT4G05100 | MYB74 |
| MYB68 | 7.475500785 | 0.00000000 | 0.000000000 | 0.000000000 | 7.396895045 | 0.000000000 | 0.042681016 | 0.000000000 | 0.000000000 | 0.000000000 | 0.03592472 | AT5G65790 | MYB68 |
| MYB3 | 10.757211712 | 2.24370630 | 0.108531103 | 0.177466813 | 7.362222126 | 0.013738644 | 0.000000000 | 0.554956234 | 0.101325051 | 0.146805007 | 0.04846043 | AT1G22640 | MYB3 |
| RAX2 | 4.319321152 | 0.44635492 | 0.012820513 | 0.318529974 | 3.390917889 | 0.150697852 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | AT2G36890 | RAX2 |
| BLJ | 3.292918817 | 0.00000000 | 0.000000000 | 0.039632918 | 3.253285899 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | AT1G14580 | BLJ |
| SCR | 3.374886967 | 0.09954593 | 0.000000000 | 0.113936058 | 3.106410590 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.05499439 | AT3G54220 | SCR |
| TLP11 | 3.773096001 | 0.00000000 | 0.282829663 | 0.099566917 | 2.040645643 | 1.154451006 | 0.110852139 | 0.000000000 | 0.072878213 | 0.011872418 | 0.00000000 | AT5G18680 | TLP11 |
| chr31 | 1.788277261 | 0.00000000 | 0.000000000 | 0.000000000 | 1.746774300 | 0.000000000 | 0.000000000 | 0.035542465 | 0.005960496 | 0.000000000 | 0.00000000 | AT1G05490 | chr31 |
| KNAT2 | 2.989769048 | 0.00000000 | 0.000000000 | 0.000000000 | 1.550327156 | 0.000000000 | 0.000000000 | 0.000000000 | 1.439441892 | 0.000000000 | 0.00000000 | AT1G70510 | KNAT2 |
| BIB | 1.308565369 | 0.00000000 | 0.000000000 | 0.000000000 | 1.308565369 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | AT3G45260 | BIB |
| RVN | 1.612171518 | 0.13624679 | 0.000000000 | 0.524083634 | 0.951841098 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | AT2G02070 | RVN |
| bZIP58 | 1.440200248 | 0.00000000 | 0.000000000 | 0.000000000 | 0.932335626 | 0.507864623 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | AT1G13600 | bZIP58 |
| AGL42 | 1.184095172 | 0.01008436 | 0.000000000 | 0.099999664 | 0.891134872 | 0.089292098 | 0.025241229 | 0.000000000 | 0.038170187 | 0.000000000 | 0.03017276 | AT5G62165 | AGL42 |
| MYB122 | 1.483576745 | 0.00000000 | 0.000000000 | 0.447435782 | 0.830218101 | 0.197116525 | 0.008806336 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | AT1G74080 | MYB122 |
| AGL102 | 0.858181111 | 0.00000000 | 0.000000000 | 0.000000000 | 0.610633263 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.24754785 | AT1G47760 | AGL102 |
| AT5G41920 | 0.822329796 | 0.06798656 | 0.164822831 | 0.000000000 | 0.496871996 | 0.037854438 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.05479397 | AT5G41920 | AT5G41920 |
| AT2G43140 | 0.476368400 | 0.00000000 | 0.000000000 | 0.014018844 | 0.410354080 | 0.000000000 | 0.000000000 | 0.016138622 | 0.000000000 | 0.000000000 | 0.03585685 | AT2G43140 | AT2G43140 |
| AT4G38340 | 0.431699948 | 0.00000000 | 0.000000000 | 0.000000000 | 0.308854429 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.12284552 | AT4G38340 | AT4G38340 |
| AT2G43280 | 0.455124566 | 0.00000000 | 0.008893077 | 0.000000000 | 0.259756676 | 0.074548095 | 0.008850991 | 0.007788138 | 0.002336449 | 0.027162978 | 0.06578816 | AT2G43280 | AT2G43280 |
| MYB70 | 0.397061782 | 0.02519915 | 0.000000000 | 0.004667502 | 0.245058739 | 0.000000000 | 0.062035046 | 0.000000000 | 0.000000000 | 0.028773722 | 0.03132762 | AT2G23290 | MYB70 |
| SIGA | 0.320776328 | 0.00000000 | 0.000000000 | 0.000000000 | 0.188299966 | 0.064815628 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.06766073 | AT1G64860 | SIGA |
| AT4G00390 | 0.160997092 | 0.01182995 | 0.000000000 | 0.012925957 | 0.136241182 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | AT4G00390 | AT4G00390 |
| AT1G18335 | 0.254368539 | 0.00000000 | 0.057588596 | 0.000000000 | 0.128300285 | 0.003564800 | 0.008778885 | 0.038600574 | 0.017535399 | 0.000000000 | 0.00000000 | AT1G18335 | AT1G18335 |
| GATA10 | 0.191792503 | 0.04265020 | 0.000000000 | 0.000000000 | 0.119306649 | 0.010554330 | 0.019281327 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | AT1G08000 | GATA10 |
| AT2G33720 | 0.128190257 | 0.00000000 | 0.000000000 | 0.000000000 | 0.116569789 | 0.000000000 | 0.000000000 | 0.011620468 | 0.000000000 | 0.000000000 | 0.00000000 | AT2G33720 | AT2G33720 |
| HAG1 | 0.218580594 | 0.00000000 | 0.034980033 | 0.000000000 | 0.111004788 | 0.018802485 | 0.007369229 | 0.000000000 | 0.000000000 | 0.018013785 | 0.02841027 | AT3G54610 | HAG1 |
| HSF3 | 0.204536317 | 0.00000000 | 0.000000000 | 0.000000000 | 0.106392311 | 0.011948034 | 0.023897923 | 0.007745382 | 0.024616629 | 0.007336018 | 0.02260002 | AT5G16820 | HSF3 |
| AT3G18870 | 0.141857130 | 0.00000000 | 0.000000000 | 0.000000000 | 0.075681907 | 0.002367195 | 0.000000000 | 0.000000000 | 0.040990658 | 0.000000000 | 0.02281737 | AT3G18870 | AT3G18870 |
| AMS | 0.077029786 | 0.00000000 | 0.029443299 | 0.000000000 | 0.047586486 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | AT2G16910 | AMS |
| 4-Sep | 0.046628927 | 0.00000000 | 0.000000000 | 0.000000000 | 0.046628927 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | AT2G03710 | 4-Sep |
| AGL62 | 0.018086588 | 0.00000000 | 0.000000000 | 0.000000000 | 0.018086588 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | AT5G60440 | AGL62 |
| AGL13 | 0.017721966 | 0.00000000 | 0.000000000 | 0.000000000 | 0.017721966 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | AT3G61120 | AGL13 |
| TDF1 | 0.014834886 | 0.00000000 | 0.000000000 | 0.000000000 | 0.014834886 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | AT3G28470 | TDF1 |
| DUO1 | 0.009040165 | 0.00000000 | 0.000000000 | 0.000000000 | 0.009040165 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | AT3G60460 | DUO1 |
In [173]:
options(repr.plot.width=6, repr.plot.height=4)
ggplot(end_rank[1:10,], aes(x=reorder(GeneName, end, decreasing = FALSE), y=end)) + geom_point(size=4)+
labs(title="Endodermis-specific TF Prioritization",x="", y = "Combined centrality score")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [209]:
write.csv(end_rank,"Endodermis_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)
In [210]:
tf_rank <- end_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])
In [211]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [212]:
options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Endodermis ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
In [213]:
tf_rank <- end_rank %>% rownames(.)
# Max 10
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
#p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
#p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
#p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
#p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
#p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
#p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])
In [214]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
#p3 <- grid.grabExpr(draw(p3))
#p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
#p7 <- grid.grabExpr(draw(p7))
#p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
#p11 <- grid.grabExpr(draw(p11))
#p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [215]:
options(repr.plot.width=24, repr.plot.height=5)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Endodermis ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,nrow=2),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,nrow=2),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,nrow=2),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
In [216]:
q1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5]) + plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
q2 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5]) + plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
q3 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5]) + plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
q1 <- grid.grabExpr(draw(q1))
q2 <- grid.grabExpr(draw(q2))
q3 <- grid.grabExpr(draw(q3))
options(repr.plot.width=16, repr.plot.height=8)
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q1,nrow=2,rel_heights = c(1, 8))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q2,nrow=2,rel_heights = c(1, 8))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q3,nrow=2,rel_heights = c(1, 8)),nrow=3)
Pericycle¶
In [217]:
per_rank <- bc_rank[which(bc_rank$per*2 > bc_rank$all),]%>% arrange(desc(per))
per_rank$GeneName <- rownames(per_rank)
In [218]:
per_rank
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | |
| AT4G29100 | 15.8083390 | 0.00000000 | 0.000000 | 0.00000000 | 0.66175063 | 8.8915794 | 3.66977328 | 0.000000000 | 2.585235682 | 0.00000000 | 0.000000000 | AT4G29100 | AT4G29100 |
| MYBC1 | 12.4042487 | 0.00000000 | 0.000000 | 0.31761261 | 2.38152429 | 8.7130171 | 0.94175584 | 0.000000000 | 0.044917605 | 0.00000000 | 0.005421276 | AT2G40970 | MYBC1 |
| LBD16 | 15.8139880 | 0.00000000 | 0.000000 | 0.04891904 | 1.05414621 | 8.4904782 | 3.89939077 | 0.000000000 | 1.882023063 | 0.00000000 | 0.439030705 | AT2G42430 | LBD16 |
| LRL1 | 14.1345131 | 0.02497473 | 2.667345 | 0.00000000 | 1.25683596 | 7.8107122 | 1.40982865 | 0.000000000 | 0.964816498 | 0.00000000 | 0.000000000 | AT2G24260 | LRL1 |
| NUC | 8.3679224 | 0.10079660 | 0.000000 | 0.88928041 | 0.19917076 | 5.8016558 | 1.32646929 | 0.000000000 | 0.050549496 | 0.00000000 | 0.000000000 | AT5G44160 | NUC |
| bZIP4 | 7.8776283 | 0.00000000 | 0.000000 | 0.00000000 | 0.02534831 | 4.0240777 | 1.58832811 | 1.456991737 | 0.782882440 | 0.00000000 | 0.000000000 | AT1G59530 | bZIP4 |
| AT3G21330 | 5.5046378 | 0.17346594 | 0.000000 | 0.00000000 | 1.23050311 | 4.0061762 | 0.09449253 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | AT3G21330 | AT3G21330 |
| LBD14 | 4.3752778 | 0.00000000 | 0.000000 | 0.00000000 | 0.30914198 | 3.9372304 | 0.00000000 | 0.128905422 | 0.000000000 | 0.00000000 | 0.000000000 | AT2G31310 | LBD14 |
| MGP | 5.7959295 | 0.40991178 | 0.000000 | 1.24980514 | 0.83953442 | 3.2193337 | 0.07734448 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | AT1G03840 | MGP |
| IDD11 | 3.1004886 | 0.00000000 | 0.000000 | 0.00000000 | 0.00000000 | 3.0767277 | 0.02376090 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | AT3G13810 | IDD11 |
| SOG1 | 4.3554378 | 0.01795298 | 0.000000 | 0.00000000 | 0.02227770 | 2.7485763 | 1.15343091 | 0.086210229 | 0.286649146 | 0.03427573 | 0.006064778 | AT1G25580 | SOG1 |
| AT1G26790 | 3.7877728 | 0.00000000 | 0.000000 | 0.00000000 | 0.00000000 | 2.6467893 | 0.51645134 | 0.000000000 | 0.624532174 | 0.00000000 | 0.000000000 | AT1G26790 | AT1G26790 |
| MYB34 | 4.1073596 | 0.15546252 | 0.000000 | 0.02221388 | 0.80373132 | 2.3809509 | 0.40781446 | 0.000000000 | 0.158807742 | 0.08922329 | 0.089155443 | AT5G60890 | MYB34 |
| GATA23 | 2.1529482 | 0.00000000 | 0.000000 | 0.00000000 | 0.02490866 | 2.1280396 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | AT5G26930 | GATA23 |
| AT4G20970 | 2.0696346 | 0.00000000 | 0.000000 | 0.00000000 | 0.00000000 | 1.9775228 | 0.03924492 | 0.000000000 | 0.052866870 | 0.00000000 | 0.000000000 | AT4G20970 | AT4G20970 |
| AT5G23280 | 3.3136758 | 0.00851619 | 0.000000 | 0.21966599 | 0.23870619 | 1.9519548 | 0.50917676 | 0.184882063 | 0.200773879 | 0.00000000 | 0.000000000 | AT5G23280 | AT5G23280 |
| AT2G35430 | 2.5748926 | 0.16604462 | 0.000000 | 0.00000000 | 0.11131578 | 1.8030207 | 0.13812910 | 0.042728542 | 0.098626974 | 0.05958352 | 0.155443322 | AT2G35430 | AT2G35430 |
| SAP | 3.1843716 | 0.08137009 | 0.000000 | 0.08645798 | 1.17590466 | 1.7565411 | 0.08409781 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | AT5G35770 | SAP |
| AT2G42040 | 2.1815010 | 0.00000000 | 0.000000 | 0.01744204 | 0.35749994 | 1.4638800 | 0.28203743 | 0.000000000 | 0.060641616 | 0.00000000 | 0.000000000 | AT2G42040 | AT2G42040 |
| ERF10 | 2.1044537 | 0.00000000 | 0.000000 | 0.00000000 | 0.64618407 | 1.2218103 | 0.01743979 | 0.219019545 | 0.000000000 | 0.00000000 | 0.000000000 | AT1G03800 | ERF10 |
| AT3G61550 | 1.2631690 | 0.00000000 | 0.000000 | 0.00000000 | 0.07384667 | 1.0378138 | 0.01225323 | 0.073173685 | 0.000000000 | 0.01383300 | 0.052248677 | AT3G61550 | AT3G61550 |
| AT3G50650 | 2.0071779 | 0.00000000 | 0.000000 | 0.00000000 | 0.00499636 | 1.0155979 | 0.42913565 | 0.368105352 | 0.189342632 | 0.00000000 | 0.000000000 | AT3G50650 | AT3G50650 |
| LBD29 | 1.0430050 | 0.00000000 | 0.000000 | 0.00000000 | 0.00000000 | 0.5794801 | 0.40783754 | 0.000000000 | 0.055687340 | 0.00000000 | 0.000000000 | AT3G58190 | LBD29 |
| WRKY67 | 0.5774885 | 0.00000000 | 0.000000 | 0.00000000 | 0.00402257 | 0.4449165 | 0.04553463 | 0.047618147 | 0.035396638 | 0.00000000 | 0.000000000 | AT1G66550 | WRKY67 |
| WOX14 | 0.3089586 | 0.00000000 | 0.000000 | 0.00000000 | 0.00000000 | 0.2527533 | 0.01499778 | 0.041207502 | 0.000000000 | 0.00000000 | 0.000000000 | AT1G20700 | WOX14 |
| AT2G20400 | 0.3926374 | 0.00000000 | 0.000000 | 0.00000000 | 0.02295462 | 0.2203607 | 0.11454819 | 0.005922467 | 0.003023639 | 0.00000000 | 0.025827801 | AT2G20400 | AT2G20400 |
| BOP2 | 0.1736959 | 0.00000000 | 0.000000 | 0.00000000 | 0.00000000 | 0.1736959 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | AT2G41370 | BOP2 |
In [219]:
options(repr.plot.width=6, repr.plot.height=4)
ggplot(per_rank[1:10,], aes(x=reorder(GeneName, per, decreasing = FALSE), y=per)) + geom_point(size=4)+
labs(title="Pericycle-specific TF Prioritization",x="", y = "Combined centrality score")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [220]:
write.csv(per_rank,"Pericycle_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)
In [221]:
tf_rank <- per_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])
In [222]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [223]:
options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Pericycle ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
In [224]:
q1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3])
q2 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3])
q3 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3])
q1 <- grid.grabExpr(draw(q1))
q2 <- grid.grabExpr(draw(q2))
q3 <- grid.grabExpr(draw(q3))
options(repr.plot.width=6, repr.plot.height=8)
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q1,nrow=2,rel_heights = c(1, 8))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q2,nrow=2,rel_heights = c(1, 8))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q3,nrow=2,rel_heights = c(1, 8)),nrow=3)
Procambium¶
In [225]:
pro_rank <- bc_rank[which(bc_rank$pro*2 > bc_rank$all),]%>% arrange(desc(pro))
pro_rank$GeneName <- rownames(pro_rank)
In [226]:
pro_rank
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | |
| AT1G61660 | 19.66448130 | 0.00000000 | 0.00000000 | 0.00000000 | 0.027513315 | 1.308861284 | 9.91179187 | 3.65891724 | 4.75739758 | 0.00000000 | 0.0000000 | AT1G61660 | AT1G61660 |
| MYB88 | 15.09234439 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.564039378 | 9.42854467 | 1.71320671 | 3.29802699 | 0.08852664 | 0.0000000 | AT2G02820 | MYB88 |
| IAA12 | 17.13838585 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.307158062 | 8.71622709 | 5.70692963 | 2.40807107 | 0.00000000 | 0.0000000 | AT1G04550 | IAA12 |
| ABO3 | 14.90735483 | 0.13198291 | 0.00000000 | 0.01259942 | 0.053741401 | 2.796666158 | 8.23950337 | 1.32021837 | 2.35264320 | 0.00000000 | 0.0000000 | AT1G66600 | ABO3 |
| RVE1 | 9.74452758 | 0.00000000 | 0.00000000 | 0.00000000 | 0.054219342 | 0.567162571 | 5.08516335 | 2.17330242 | 1.65991743 | 0.09819062 | 0.1065718 | AT5G17300 | RVE1 |
| HB18 | 5.72041091 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.000000000 | 4.23145083 | 0.27791535 | 1.21104472 | 0.00000000 | 0.0000000 | AT1G70920 | HB18 |
| HAT9 | 4.97434732 | 0.00000000 | 0.00000000 | 0.12209428 | 0.008108846 | 0.314151011 | 3.59905098 | 0.01695377 | 0.91398844 | 0.00000000 | 0.0000000 | AT2G22800 | HAT9 |
| AT2G40200 | 4.47922324 | 0.00000000 | 0.08663353 | 0.00000000 | 0.000000000 | 0.018974084 | 2.89717729 | 0.01930762 | 1.45713071 | 0.00000000 | 0.0000000 | AT2G40200 | AT2G40200 |
| NAC080 | 5.27661055 | 0.00000000 | 0.00000000 | 0.00000000 | 0.028855655 | 0.383149699 | 2.83167375 | 0.02084784 | 1.70681657 | 0.07513935 | 0.2301277 | AT5G07680 | NAC080 |
| AT1G51200 | 3.28825040 | 0.10084754 | 0.30540427 | 0.00000000 | 0.046603601 | 0.349951394 | 1.71218924 | 0.46577503 | 0.17420184 | 0.01962096 | 0.1136565 | AT1G51200 | AT1G51200 |
| AT1G10610 | 0.34039505 | 0.01091073 | 0.00000000 | 0.00000000 | 0.002011285 | 0.030931908 | 0.18809118 | 0.03375702 | 0.07469293 | 0.00000000 | 0.0000000 | AT1G10610 | AT1G10610 |
| FRS12 | 0.23369051 | 0.00000000 | 0.00000000 | 0.00000000 | 0.069598068 | 0.000000000 | 0.16409244 | 0.00000000 | 0.00000000 | 0.00000000 | 0.0000000 | AT5G18960 | FRS12 |
| AT1G74120 | 0.05478042 | 0.00000000 | 0.00000000 | 0.00000000 | 0.009245547 | 0.009384739 | 0.03615014 | 0.00000000 | 0.00000000 | 0.00000000 | 0.0000000 | AT1G74120 | AT1G74120 |
In [227]:
options(repr.plot.width=6, repr.plot.height=4)
ggplot(pro_rank[1:10,], aes(x=reorder(GeneName, pro, decreasing = FALSE), y=pro)) + geom_point(size=4)+
labs(title="Procambium-specific TF Prioritization",x="", y = "Combined centrality score")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [228]:
write.csv(pro_rank,"Procambium_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)
In [229]:
tf_rank <- pro_rank %>% rownames(.)
# Max 30
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_blank(tf_rank[15])
p4 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p5 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p6 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_blank(tf_rank[15])
p7 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p8 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p9 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_blank(tf_rank[15])
In [230]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [231]:
options(repr.plot.width=24, repr.plot.height=10)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Procambium ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,nrow=3),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p4,p5,p6,nrow=3),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p7,p8,p9,nrow=3),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
Phloem¶
In [232]:
phl_rank <- bc_rank[which(bc_rank$phl*2 > bc_rank$all),]%>% arrange(desc(phl))
phl_rank$GeneName <- rownames(phl_rank)
In [233]:
phl_rank
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | |
| APL | 14.609318871 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.169637300 | 1.11060353 | 0.000000000 | 13.329078042 | 0.00000000 | 0.000000000 | AT1G79430 | APL |
| AT3G12730 | 9.025308271 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.01251256 | 0.000000000 | 9.012795710 | 0.00000000 | 0.000000000 | AT3G12730 | AT3G12730 |
| AT2G03500 | 8.317163951 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.092932605 | 0.01861757 | 0.000000000 | 8.205613779 | 0.00000000 | 0.000000000 | AT2G03500 | AT2G03500 |
| DOF2.4 | 13.209764619 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.151092484 | 4.50853801 | 0.765591862 | 7.784542259 | 0.00000000 | 0.000000000 | AT2G37590 | DOF2.4 |
| HCA2 | 7.846192271 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.089558939 | 0.14943530 | 0.000000000 | 7.607198033 | 0.00000000 | 0.000000000 | AT5G62940 | HCA2 |
| AT4G37180 | 8.352277506 | 0.00000000 | 0.000000000 | 0.00000000 | 0.029722861 | 0.369357463 | 0.31110514 | 0.098356502 | 7.455714471 | 0.07976729 | 0.008253774 | AT4G37180 | AT4G37180 |
| NAC020 | 8.447018279 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.041412518 | 1.44054230 | 0.211972030 | 6.753091429 | 0.00000000 | 0.000000000 | AT1G54330 | NAC020 |
| DOF6 | 8.501982872 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.034094870 | 0.32764725 | 1.414083851 | 6.726156901 | 0.00000000 | 0.000000000 | AT3G45610 | DOF6 |
| DAR2 | 8.256303735 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.170725842 | 1.37175451 | 0.000000000 | 6.713823384 | 0.00000000 | 0.000000000 | AT2G39830 | DAR2 |
| AT2G44940 | 12.938438821 | 0.00000000 | 0.000000000 | 0.00000000 | 0.046800693 | 2.660469564 | 3.53822594 | 0.038214102 | 6.654728520 | 0.00000000 | 0.000000000 | AT2G44940 | AT2G44940 |
| NAC057 | 6.555534970 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.009991746 | 0.10620947 | 0.000000000 | 6.439333749 | 0.00000000 | 0.000000000 | AT3G17730 | NAC057 |
| AT5G02460 | 8.900886315 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.249027491 | 2.09833416 | 0.367268069 | 6.186256597 | 0.00000000 | 0.000000000 | AT5G02460 | AT5G02460 |
| AT5G41380 | 6.305882379 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00623467 | 0.131992268 | 6.167655442 | 0.00000000 | 0.000000000 | AT5G41380 | AT5G41380 |
| GATA20 | 5.519668903 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.122702828 | 0.12870372 | 0.000000000 | 5.268262354 | 0.00000000 | 0.000000000 | AT2G18380 | GATA20 |
| AT1G63820 | 6.294861023 | 0.00000000 | 0.000000000 | 0.00000000 | 0.002506270 | 1.895351998 | 0.33273546 | 0.000000000 | 4.064267291 | 0.00000000 | 0.000000000 | AT1G63820 | AT1G63820 |
| bZIP19 | 5.433142219 | 0.14461615 | 0.054054845 | 0.04748350 | 0.055169399 | 1.224722642 | 0.25748640 | 0.021920640 | 3.614756643 | 0.01293200 | 0.000000000 | AT4G35040 | bZIP19 |
| WRKY32 | 5.528816801 | 0.00000000 | 0.000000000 | 0.01719560 | 0.046284481 | 0.610058142 | 1.59914599 | 0.000000000 | 3.169889940 | 0.02621422 | 0.060028433 | AT4G30935 | WRKY32 |
| AT1G49560 | 4.950654130 | 0.70780773 | 0.000000000 | 0.27927220 | 0.327016355 | 0.000000000 | 0.01743979 | 0.023209300 | 2.998822472 | 0.26212902 | 0.334957268 | AT1G49560 | AT1G49560 |
| NAC045 | 4.560162247 | 0.00000000 | 0.000000000 | 0.00000000 | 0.004001335 | 0.045041485 | 0.80063558 | 0.857306888 | 2.846099864 | 0.00707710 | 0.000000000 | AT3G03200 | NAC045 |
| REM22 | 3.158570177 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.026117949 | 0.30064107 | 0.088825649 | 2.742985514 | 0.00000000 | 0.000000000 | AT3G17010 | REM22 |
| AGL15 | 3.119741418 | 0.00000000 | 0.000000000 | 0.00000000 | 0.005012539 | 0.000000000 | 0.14636004 | 0.286023006 | 2.682345836 | 0.00000000 | 0.000000000 | AT5G13790 | AGL15 |
| MYB10 | 3.548157336 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 1.04973108 | 0.133287669 | 2.365138584 | 0.00000000 | 0.000000000 | AT3G12820 | MYB10 |
| AS1 | 3.859403778 | 0.04011552 | 0.005994526 | 0.10588835 | 0.000000000 | 0.200370039 | 1.00935074 | 0.426654519 | 2.071030084 | 0.00000000 | 0.000000000 | AT2G37630 | AS1 |
| AT5G63700 | 3.170561138 | 0.02108128 | 0.000000000 | 1.08427812 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 2.052123273 | 0.01307847 | 0.000000000 | AT5G63700 | AT5G63700 |
| WRI4 | 2.902957478 | 0.03301368 | 0.000000000 | 0.00000000 | 0.041343472 | 0.081580848 | 0.18242721 | 0.000000000 | 1.530986398 | 0.74826947 | 0.285336401 | AT1G79700 | WRI4 |
| SPL10 | 1.592789423 | 0.00000000 | 0.000000000 | 0.00000000 | 0.008023905 | 0.000000000 | 0.10614823 | 0.000000000 | 1.409442027 | 0.00000000 | 0.069175260 | AT1G27370 | SPL10 |
| ROS4 | 2.113804135 | 0.05327527 | 0.039457980 | 0.01164425 | 0.010938601 | 0.449028597 | 0.05642218 | 0.105796605 | 1.354810136 | 0.03243051 | 0.000000000 | AT3G14980 | ROS4 |
| WOX2 | 1.238256758 | 0.00000000 | 0.000000000 | 0.01974013 | 0.025250886 | 0.000000000 | 0.00000000 | 0.000000000 | 1.193265746 | 0.00000000 | 0.000000000 | AT5G59340 | WOX2 |
| NAC086 | 1.212685324 | 0.00000000 | 0.000000000 | 0.00000000 | 0.006567017 | 0.000000000 | 0.03657601 | 0.000000000 | 1.169542297 | 0.00000000 | 0.000000000 | AT5G17260 | NAC086 |
| bHLH11 | 1.453746048 | 0.01288040 | 0.000000000 | 0.00000000 | 0.000000000 | 0.185918452 | 0.02812625 | 0.000000000 | 1.062357581 | 0.02479862 | 0.139664742 | AT4G36060 | bHLH11 |
| ARF11 | 0.901058214 | 0.00000000 | 0.000000000 | 0.00000000 | 0.011869821 | 0.033735914 | 0.00000000 | 0.220173338 | 0.635279141 | 0.00000000 | 0.000000000 | AT2G46530 | ARF11 |
| SIGC | 0.851642089 | 0.02497473 | 0.000000000 | 0.01711032 | 0.010735557 | 0.015590945 | 0.15531491 | 0.077881384 | 0.539857293 | 0.01017694 | 0.000000000 | AT3G53920 | SIGC |
| AT1G64530 | 0.957009198 | 0.04765862 | 0.078767526 | 0.09575725 | 0.092565457 | 0.027427958 | 0.05342428 | 0.003879403 | 0.519560887 | 0.03796782 | 0.000000000 | AT1G64530 | AT1G64530 |
| AT1G58220 | 0.794464816 | 0.00000000 | 0.011989052 | 0.00000000 | 0.019928994 | 0.000000000 | 0.09407689 | 0.198104371 | 0.414654005 | 0.01106640 | 0.044645107 | AT1G58220 | AT1G58220 |
| NF-YB3 | 0.296581384 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.01478168 | 0.013385153 | 0.268414553 | 0.00000000 | 0.000000000 | AT4G14540 | NF-YB3 |
| AT5G63080 | 0.298967001 | 0.00000000 | 0.000000000 | 0.00000000 | 0.018766764 | 0.000000000 | 0.01861757 | 0.000000000 | 0.204321653 | 0.05029767 | 0.006963346 | AT5G63080 | AT5G63080 |
| AT3G46070 | 0.159354155 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.115534733 | 0.04381942 | 0.000000000 | AT3G46070 | AT3G46070 |
| AT4G03250 | 0.130834020 | 0.00000000 | 0.000000000 | 0.00000000 | 0.007332134 | 0.000000000 | 0.00000000 | 0.000000000 | 0.096468022 | 0.00000000 | 0.027033864 | AT4G03250 | AT4G03250 |
| AT3G06220 | 0.094004217 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.094004217 | 0.00000000 | 0.000000000 | AT3G06220 | AT3G06220 |
| AT3G52270 | 0.009410419 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.009410419 | 0.00000000 | 0.000000000 | AT3G52270 | AT3G52270 |
In [234]:
options(repr.plot.width=6, repr.plot.height=4)
ggplot(phl_rank[1:10,], aes(x=reorder(GeneName, phl, decreasing = FALSE), y=phl)) + geom_point(size=4)+
labs(title="Phloem-specific TF Prioritization",x="", y = "Combined centrality score")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [235]:
write.csv(phl_rank,"Phloem_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)
In [236]:
tf_rank <- phl_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])
In [237]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [238]:
options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Phloem ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
Xylem¶
In [239]:
xyl_rank <- bc_rank[which(bc_rank$xyl*2 > bc_rank$all),]%>% arrange(desc(xyl))
xyl_rank$GeneName <- rownames(xyl_rank)
In [240]:
xyl_rank
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | |
| AT1G68810 | 17.063919 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.08165321 | 5.145337642 | 11.157002 | 0.67992676 | 0.000000000 | 0.00000000 | AT1G68810 | AT1G68810 |
| DOF2 | 15.222923 | 0.00000000 | 0.000000000 | 0.0000000 | 1.142731297 | 0.00000000 | 0.110262281 | 10.647285 | 0.08692365 | 0.005533199 | 3.23018731 | AT3G21270 | DOF2 |
| MYB46 | 9.946099 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.000000000 | 9.946099 | 0.00000000 | 0.000000000 | 0.00000000 | AT5G12870 | MYB46 |
| VND7 | 9.855090 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.018704010 | 9.836386 | 0.00000000 | 0.000000000 | 0.00000000 | AT1G71930 | VND7 |
| VND2 | 10.375300 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.560639801 | 9.593416 | 0.22124389 | 0.000000000 | 0.00000000 | AT4G36160 | VND2 |
| MYB83 | 8.984786 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.000000000 | 8.984786 | 0.00000000 | 0.000000000 | 0.00000000 | AT3G08500 | MYB83 |
| VND5 | 8.500834 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.007369229 | 8.493465 | 0.00000000 | 0.000000000 | 0.00000000 | AT1G62700 | VND5 |
| XND1 | 11.066321 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.01688947 | 1.997901734 | 8.389225 | 0.66230437 | 0.000000000 | 0.00000000 | AT5G64530 | XND1 |
| VND1 | 8.411626 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.027164576 | 8.384461 | 0.00000000 | 0.000000000 | 0.00000000 | AT2G18060 | VND1 |
| VND3 | 10.804766 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 2.267029156 | 8.234689 | 0.30304808 | 0.000000000 | 0.00000000 | AT5G66300 | VND3 |
| VND4 | 8.264343 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.059818255 | 8.204524 | 0.00000000 | 0.000000000 | 0.00000000 | AT1G12260 | VND4 |
| AT5G03510 | 14.364263 | 0.33450117 | 0.105916180 | 0.0000000 | 0.100592700 | 0.00509819 | 0.012500000 | 7.843447 | 0.13509714 | 2.644959201 | 3.18215109 | AT5G03510 | AT5G03510 |
| IAA6 | 7.449006 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.050609684 | 7.286594 | 0.11180218 | 0.000000000 | 0.00000000 | AT1G52830 | IAA6 |
| BHLH32 | 12.693846 | 0.00000000 | 0.000000000 | 0.0000000 | 0.991284303 | 3.13466547 | 1.222152495 | 7.077379 | 0.26836559 | 0.000000000 | 0.00000000 | AT3G25710 | BHLH32 |
| IAA8 | 13.643534 | 0.13344412 | 0.000000000 | 0.4868603 | 0.052802034 | 0.13199661 | 3.677422098 | 6.967160 | 0.44097225 | 0.573551336 | 1.17932503 | AT2G22670 | IAA8 |
| AT1G68200 | 6.891704 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.000000000 | 6.891704 | 0.00000000 | 0.000000000 | 0.00000000 | AT1G68200 | AT1G68200 |
| LBD18 | 8.324306 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.000000000 | 6.765355 | 0.00000000 | 0.000000000 | 1.55895123 | AT2G45420 | LBD18 |
| VND6 | 6.042459 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.000000000 | 6.042459 | 0.00000000 | 0.000000000 | 0.00000000 | AT5G62380 | VND6 |
| HB31 | 6.000334 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.000000000 | 6.000334 | 0.00000000 | 0.000000000 | 0.00000000 | AT1G14440 | HB31 |
| AT4G13620 | 8.301733 | 0.00000000 | 0.092439132 | 0.1064019 | 0.239266494 | 1.97331533 | 0.063265152 | 5.431450 | 0.39559475 | 0.000000000 | 0.00000000 | AT4G13620 | AT4G13620 |
| AT1G27660 | 9.577448 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 3.24492666 | 0.841223229 | 5.408532 | 0.08276668 | 0.000000000 | 0.00000000 | AT1G27660 | AT1G27660 |
| IAA31 | 10.120873 | 0.00000000 | 1.606032028 | 0.0000000 | 0.000000000 | 0.00000000 | 1.732273373 | 5.406830 | 1.37573770 | 0.000000000 | 0.00000000 | AT3G17600 | IAA31 |
| ZHD3 | 5.381129 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.000000000 | 5.381129 | 0.00000000 | 0.000000000 | 0.00000000 | AT2G02540 | ZHD3 |
| BBX31 | 8.290061 | 0.04442607 | 0.007878508 | 0.1233265 | 0.000000000 | 0.00000000 | 0.134382077 | 5.301686 | 0.10920589 | 2.263892239 | 0.30526388 | AT3G21890 | BBX31 |
| AT1G66810 | 5.709176 | 0.20362674 | 0.056435818 | 0.0000000 | 0.005474121 | 0.00000000 | 0.000000000 | 5.189221 | 0.00000000 | 0.254418805 | 0.00000000 | AT1G66810 | AT1G66810 |
| BEE2 | 5.624491 | 0.00000000 | 0.000000000 | 0.0000000 | 0.415267859 | 0.00000000 | 0.007587322 | 5.145188 | 0.02869051 | 0.000000000 | 0.02775774 | AT4G36540 | BEE2 |
| HAT14 | 8.218759 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 1.816622909 | 5.059303 | 1.34283274 | 0.000000000 | 0.00000000 | AT5G06710 | HAT14 |
| LBD31 | 5.036510 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.00000000 | 0.000000000 | 5.036510 | 0.00000000 | 0.000000000 | 0.00000000 | AT4G00210 | LBD31 |
| ASL9 | 8.517421 | 0.00000000 | 0.000000000 | 0.0000000 | 0.000000000 | 0.04799636 | 2.302923811 | 4.789639 | 1.37686170 | 0.000000000 | 0.00000000 | AT1G16530 | ASL9 |
| AP3 | 5.637310 | 0.00000000 | 0.000000000 | 0.0000000 | 0.036477198 | 0.00000000 | 0.737317351 | 4.705632 | 0.15788376 | 0.000000000 | 0.00000000 | AT3G54340 | AP3 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| GIF3 | 2.14882356 | 0.000000000 | 0.000000000 | 0.000000000 | 0.020865600 | 0.02852985 | 0.023589815 | 1.94922756 | 0.061886118 | 0.000000000 | 0.064724627 | AT4G00850 | GIF3 |
| PIF4 | 2.05470876 | 0.000000000 | 0.000000000 | 0.000000000 | 0.015390204 | 0.00000000 | 0.000000000 | 1.86051723 | 0.157779289 | 0.000000000 | 0.021022038 | AT2G43010 | PIF4 |
| AT3G10470 | 2.93087103 | 0.000000000 | 0.360962672 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 1.85166194 | 0.000000000 | 0.718246419 | 0.000000000 | AT3G10470 | AT3G10470 |
| AT1G21780 | 2.82992881 | 0.020964323 | 0.018434930 | 0.000000000 | 0.029889794 | 0.00000000 | 0.018748225 | 1.77579373 | 0.027047591 | 0.522926450 | 0.416123760 | AT1G21780 | AT1G21780 |
| HB34 | 1.96190545 | 0.046591360 | 0.005994526 | 0.000000000 | 0.013019220 | 0.00000000 | 0.053611881 | 1.72574368 | 0.099670453 | 0.017274321 | 0.000000000 | AT3G28920 | HB34 |
| AT3G22560 | 1.67685834 | 0.000000000 | 0.074410480 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 1.60244786 | 0.000000000 | 0.000000000 | 0.000000000 | AT3G22560 | AT3G22560 |
| NAC050 | 2.68072857 | 0.000000000 | 0.000000000 | 0.000000000 | 0.017629250 | 0.00000000 | 0.000000000 | 1.58695913 | 0.130838958 | 0.179013418 | 0.766287814 | AT3G10480 | NAC050 |
| AT2G22200 | 2.67811089 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 1.55244143 | 0.000000000 | 0.000000000 | 1.125669465 | AT2G22200 | AT2G22200 |
| AT4G00940 | 2.85835376 | 0.000000000 | 0.000000000 | 0.000000000 | 0.596230663 | 0.57740568 | 0.060214765 | 1.52802016 | 0.096482493 | 0.000000000 | 0.000000000 | AT4G00940 | AT4G00940 |
| BZIP24 | 1.80081118 | 0.000000000 | 0.117864451 | 0.000000000 | 0.000000000 | 0.00000000 | 0.094317695 | 1.51822676 | 0.070402269 | 0.000000000 | 0.000000000 | AT3G51960 | BZIP24 |
| AT1G24040 | 1.52692785 | 0.004281654 | 0.000000000 | 0.000000000 | 0.003006310 | 0.02930774 | 0.024636131 | 1.46192338 | 0.000000000 | 0.003772636 | 0.000000000 | AT1G24040 | AT1G24040 |
| SPL7 | 1.99826008 | 0.028051656 | 0.148913431 | 0.000000000 | 0.148773469 | 0.09874422 | 0.155315846 | 1.35297469 | 0.049655748 | 0.000000000 | 0.015831015 | AT5G18830 | SPL7 |
| FBH3 | 2.60731470 | 0.051097139 | 0.067833848 | 0.010054895 | 0.230264834 | 0.41209024 | 0.270368947 | 1.33856507 | 0.157235638 | 0.012140391 | 0.057663692 | AT1G51140 | FBH3 |
| MMD1 | 1.35269403 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.07271717 | 0.000000000 | 1.27997686 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G66170 | MMD1 |
| MYB25 | 1.41962871 | 0.123608144 | 0.000000000 | 0.075543880 | 0.041880808 | 0.02851802 | 0.000000000 | 1.07564699 | 0.018133775 | 0.000000000 | 0.056297098 | AT2G39880 | MYB25 |
| ARR9 | 1.56981248 | 0.000000000 | 0.125849461 | 0.034916399 | 0.163930017 | 0.00356480 | 0.048264989 | 1.00369692 | 0.000000000 | 0.104239410 | 0.085350489 | AT3G57040 | ARR9 |
| AT5G65130 | 1.14236293 | 0.006215890 | 0.105834398 | 0.000000000 | 0.000000000 | 0.00000000 | 0.032696900 | 0.65148420 | 0.235301014 | 0.000000000 | 0.110830527 | AT5G65130 | AT5G65130 |
| AT1G24610 | 0.85998486 | 0.036055793 | 0.017588729 | 0.000000000 | 0.096322882 | 0.03404991 | 0.002485224 | 0.57801360 | 0.031744490 | 0.005533199 | 0.058191037 | AT1G24610 | AT1G24610 |
| AT3G10760 | 1.04278963 | 0.000000000 | 0.000000000 | 0.230068493 | 0.157374112 | 0.03089667 | 0.013738937 | 0.54551377 | 0.033755758 | 0.031441890 | 0.000000000 | AT3G10760 | AT3G10760 |
| AT1G26590 | 0.84195835 | 0.016455616 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.137925211 | 0.52692786 | 0.160649656 | 0.000000000 | 0.000000000 | AT1G26590 | AT1G26590 |
| AT5G06770 | 0.69082427 | 0.032487367 | 0.051353273 | 0.042040194 | 0.078298079 | 0.00000000 | 0.024592910 | 0.45498012 | 0.000000000 | 0.000000000 | 0.007072337 | AT5G06770 | AT5G06770 |
| AT5G60142 | 0.52269340 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.068949723 | 0.45374368 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G60142 | AT5G60142 |
| AGL64 | 0.39244926 | 0.019026310 | 0.101611204 | 0.000000000 | 0.000000000 | 0.00509819 | 0.000000000 | 0.26671356 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G29962 | AGL64 |
| AT4G08250 | 0.20019402 | 0.000000000 | 0.009090501 | 0.000000000 | 0.021199072 | 0.00000000 | 0.000000000 | 0.16613182 | 0.000000000 | 0.003772636 | 0.000000000 | AT4G08250 | AT4G08250 |
| MYB97 | 0.19123764 | 0.029281127 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.16195651 | 0.000000000 | 0.000000000 | 0.000000000 | AT4G26930 | MYB97 |
| AT1G76880 | 0.14911038 | 0.000000000 | 0.020882129 | 0.032590817 | 0.000000000 | 0.00000000 | 0.000000000 | 0.09563743 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G76880 | AT1G76880 |
| SDG20 | 0.12098223 | 0.000000000 | 0.000000000 | 0.000000000 | 0.016856592 | 0.00000000 | 0.006234670 | 0.07746889 | 0.005491118 | 0.014930955 | 0.000000000 | AT3G03750 | SDG20 |
| BBX32 | 0.08976690 | 0.000000000 | 0.020487280 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.06927962 | 0.000000000 | 0.000000000 | 0.000000000 | AT3G21150 | BBX32 |
| OFP11 | 0.13539950 | 0.012880404 | 0.000000000 | 0.000000000 | 0.005201742 | 0.00000000 | 0.000000000 | 0.06846599 | 0.008106896 | 0.040744467 | 0.000000000 | AT4G14860 | OFP11 |
| WRKY50 | 0.04421566 | 0.000000000 | 0.000000000 | 0.006993083 | 0.002195432 | 0.00000000 | 0.005013669 | 0.03001347 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G26170 | WRKY50 |
In [241]:
options(repr.plot.width=6, repr.plot.height=4)
ggplot(xyl_rank[1:10,], aes(x=reorder(GeneName, xyl, decreasing = FALSE), y=xyl)) + geom_point(size=4)+
labs(title="Xylem-specific TF Prioritization",x="", y = "Combined centrality score")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [242]:
write.csv(xyl_rank,"Xylem_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)
In [243]:
tf_rank <- xyl_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])
In [244]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [245]:
options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Xylem ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
LRC¶
In [246]:
lrc_rank <- bc_rank[which(bc_rank$lrc*2 > bc_rank$all),]%>% arrange(desc(lrc))
lrc_rank$GeneName <- rownames(lrc_rank)
In [247]:
lrc_rank
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | |
| ARF16 | 12.0950716 | 1.18872804 | 0.22625031 | 0.33645643 | 0.027554161 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 7.5445870 | 2.771495746 | AT4G30080 | ARF16 |
| MIF3 | 8.2345450 | 0.00000000 | 0.00000000 | 0.00000000 | 0.362226980 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 4.5448080 | 3.327510060 | AT1G18835 | MIF3 |
| OFP6 | 5.7370542 | 1.28748706 | 0.00000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 4.4092282 | 0.040338972 | AT3G52525 | OFP6 |
| AT1G68920 | 7.9078642 | 0.08594922 | 0.00000000 | 0.09890113 | 0.159415359 | 0.000000000 | 0.02936865 | 0.000000000 | 0.06845612 | 4.3273197 | 3.138453997 | AT1G68920 | AT1G68920 |
| ARF6 | 7.0356258 | 0.34023062 | 0.00000000 | 0.83179872 | 0.025568139 | 0.102453827 | 0.03016845 | 0.012007659 | 0.00000000 | 3.7084397 | 1.984958693 | AT1G30330 | ARF6 |
| AT1G74840 | 6.3189942 | 0.39449532 | 0.33377213 | 0.64192249 | 0.182237084 | 0.258874442 | 0.01970890 | 0.336033911 | 0.03505829 | 3.6991153 | 0.417776294 | AT1G74840 | AT1G74840 |
| PS1 | 6.7635642 | 1.62508853 | 0.00000000 | 0.24265219 | 1.351463399 | 0.043007414 | 0.00000000 | 0.061090339 | 0.00000000 | 3.4402623 | 0.000000000 | AT1G34355 | PS1 |
| AT1G05805 | 4.6833400 | 0.34742020 | 0.03866828 | 0.01319699 | 0.000000000 | 0.000000000 | 0.00000000 | 0.005168063 | 0.00000000 | 3.1613170 | 1.117569481 | AT1G05805 | AT1G05805 |
| MYB3R-4 | 3.4926131 | 0.09540410 | 0.02417553 | 0.09236018 | 0.106512672 | 0.039449335 | 0.03402199 | 0.202288197 | 0.06203470 | 2.6981916 | 0.138174819 | AT5G11510 | MYB3R-4 |
| HMGB4 | 5.2893067 | 0.35598989 | 0.27142885 | 0.00000000 | 0.040561847 | 0.478249091 | 0.36303172 | 0.600467999 | 0.34378116 | 2.6669185 | 0.168877560 | AT2G17560 | HMGB4 |
| AT1G77200 | 4.9305036 | 1.20595284 | 0.27706781 | 0.38909568 | 0.064591198 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 2.5328168 | 0.460979324 | AT1G77200 | AT1G77200 |
| HMGB3 | 4.0632196 | 0.16788386 | 0.07272401 | 0.00000000 | 0.097275219 | 0.268261140 | 0.30255812 | 0.059236843 | 0.46156669 | 2.2499624 | 0.383751354 | AT1G20696 | HMGB3 |
| WRKY42 | 2.5967614 | 0.12152806 | 0.26628613 | 0.06480232 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 1.7317216 | 0.412423348 | AT4G04450 | WRKY42 |
| NGA3 | 2.8344349 | 0.00000000 | 0.00000000 | 0.09480066 | 0.658761113 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 1.5511749 | 0.529698233 | AT1G01030 | NGA3 |
| AT1G69030 | 3.0068851 | 0.20593015 | 0.05965352 | 0.19553401 | 0.052897688 | 0.003536786 | 0.19161073 | 0.363297787 | 0.07958571 | 1.5188214 | 0.336017317 | AT1G69030 | AT1G69030 |
| SDG37 | 2.1434665 | 0.15521109 | 0.00000000 | 0.09547487 | 0.121836047 | 0.067726936 | 0.10576622 | 0.070805170 | 0.04402488 | 1.4826213 | 0.000000000 | AT2G17900 | SDG37 |
| CHR24 | 2.4670641 | 0.12112585 | 0.00000000 | 0.26181318 | 0.039662206 | 0.271754032 | 0.31931830 | 0.068125364 | 0.10001367 | 1.2852515 | 0.000000000 | AT5G63950 | CHR24 |
| GRF2 | 2.1368396 | 0.23429787 | 0.11065419 | 0.12460868 | 0.019397826 | 0.061123489 | 0.02088668 | 0.206450002 | 0.07330615 | 1.1823258 | 0.103788929 | AT4G37740 | GRF2 |
| AT4G17780 | 2.1044433 | 0.20170540 | 0.00000000 | 0.02840583 | 0.000000000 | 0.000000000 | 0.25350154 | 0.000000000 | 0.39778033 | 1.1635464 | 0.059503768 | AT4G17780 | AT4G17780 |
| HDG1 | 2.2688805 | 0.14863364 | 0.77650804 | 0.10046804 | 0.000000000 | 0.000000000 | 0.00000000 | 0.034232993 | 0.00000000 | 1.1454809 | 0.063556879 | AT3G61150 | HDG1 |
| PYE | 1.7186163 | 0.08636786 | 0.00000000 | 0.00000000 | 0.052953000 | 0.037878690 | 0.06569731 | 0.091683869 | 0.18137128 | 0.9516189 | 0.251045311 | AT3G47640 | PYE |
| HDG2 | 0.9656596 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.9237737 | 0.041885833 | AT1G05230 | HDG2 |
| DAR7 | 1.3986801 | 0.32460308 | 0.03906313 | 0.00000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.8387556 | 0.196258323 | AT5G66610 | DAR7 |
| CIB5 | 1.3518636 | 0.07206012 | 0.16948092 | 0.09737207 | 0.049520665 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.7881753 | 0.175254587 | AT1G26260 | CIB5 |
| AT2G29065 | 1.0696880 | 0.06699083 | 0.10703730 | 0.01235298 | 0.009812924 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.5863505 | 0.287143468 | AT2G29065 | AT2G29065 |
| AGL17 | 0.8875874 | 0.18370943 | 0.15699146 | 0.04185739 | 0.030998529 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.4560552 | 0.017975340 | AT2G22630 | AGL17 |
| FRS1 | 0.7471507 | 0.03468916 | 0.09057433 | 0.00000000 | 0.003006310 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.4173614 | 0.201519516 | AT4G19990 | FRS1 |
| NAC063 | 0.5951431 | 0.00000000 | 0.05237134 | 0.00000000 | 0.057086763 | 0.007320087 | 0.00000000 | 0.093644020 | 0.00000000 | 0.3680460 | 0.016674827 | AT3G55210 | NAC063 |
| SPL13A | 0.4377265 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.03258455 | 0.035046623 | 0.10520033 | 0.2578227 | 0.007072337 | AT5G50570 | SPL13A |
In [248]:
options(repr.plot.width=6, repr.plot.height=4)
ggplot(lrc_rank[1:10,], aes(x=reorder(GeneName, lrc, decreasing = FALSE), y=lrc)) + geom_point(size=4)+
labs(title="LRC-specific TF Prioritization",x="", y = "Combined centrality score")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [249]:
write.csv(lrc_rank,"LRC_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)
In [250]:
tf_rank <- lrc_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])
In [251]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [252]:
options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Lateral Root Cap ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
Columella¶
In [253]:
col_rank <- bc_rank[which(bc_rank$col*2 > bc_rank$all),]%>% arrange(desc(col))
col_rank$GeneName <- rownames(col_rank)
In [254]:
col_rank
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | |
| IAA20 | 13.588308 | 0.10745453 | 0.000000000 | 0.696016725 | 1.856104365 | 0.031870533 | 0.00000000 | 0.08922763 | 0.00000000 | 0.77617173 | 10.031463 | AT2G46990 | IAA20 |
| NAC042 | 17.898571 | 0.01132391 | 0.000000000 | 0.036494981 | 0.000000000 | 0.000000000 | 0.00000000 | 1.33451875 | 0.00000000 | 7.48901508 | 9.027218 | AT2G43000 | NAC042 |
| NTT | 10.295228 | 0.04833774 | 0.000000000 | 0.254229735 | 0.289057120 | 0.000000000 | 0.03025490 | 1.83925838 | 0.06624195 | 0.07987596 | 7.687972 | AT3G57670 | NTT |
| RAP2.1 | 14.493280 | 0.15766362 | 0.000000000 | 1.586003647 | 0.076931823 | 0.110922912 | 0.00000000 | 4.34137064 | 0.00000000 | 0.95389876 | 7.266489 | AT1G46768 | RAP2.1 |
| AT3G52440 | 8.270872 | 0.00000000 | 0.000000000 | 0.066526983 | 0.000000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.00000000 | 1.01592556 | 7.188419 | AT3G52440 | AT3G52440 |
| AT3G60670 | 10.858268 | 0.11105965 | 0.000000000 | 1.252688213 | 1.273003034 | 1.358361090 | 0.00000000 | 0.05930035 | 0.00000000 | 0.51137037 | 6.292485 | AT3G60670 | AT3G60670 |
| WRKY33 | 9.821172 | 0.58635433 | 0.363985928 | 0.598834628 | 0.321301841 | 0.005903982 | 0.18906655 | 0.15253096 | 0.13432684 | 1.63949253 | 5.829374 | AT2G38470 | WRKY33 |
| FBH4 | 8.169902 | 0.06958798 | 0.050431699 | 0.139536317 | 0.018072134 | 0.000000000 | 0.00000000 | 0.00000000 | 0.00000000 | 2.15983352 | 5.732440 | AT2G42280 | FBH4 |
| LBD41 | 11.083193 | 0.22420917 | 0.078341998 | 0.048795815 | 0.072010563 | 0.012921525 | 0.62329764 | 0.85063060 | 0.26662565 | 3.35964330 | 5.546717 | AT3G02550 | LBD41 |
| NAC010 | 10.133545 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | 3.57057093 | 0.11705832 | 0.91760880 | 5.528307 | AT1G28470 | NAC010 |
| AT3G25790 | 6.803892 | 0.30672051 | 0.006301103 | 1.105426716 | 0.000000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.00000000 | 0.49668295 | 4.888761 | AT3G25790 | AT3G25790 |
| ASL1 | 9.030990 | 0.26223195 | 0.058130592 | 0.079723968 | 0.000000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.00000000 | 3.87316468 | 4.757739 | AT5G66870 | ASL1 |
| MAF5 | 7.478458 | 0.02831885 | 0.000000000 | 0.000000000 | 0.000000000 | 0.668468676 | 1.15603009 | 0.07523599 | 0.40083767 | 0.39249355 | 4.757073 | AT5G65080 | MAF5 |
| AT2G35910 | 7.223261 | 0.84020710 | 0.062393108 | 0.041531893 | 0.000000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.00000000 | 1.74032865 | 4.538800 | AT2G35910 | AT2G35910 |
| SNI1 | 7.421668 | 1.31746160 | 0.000000000 | 0.118772868 | 0.072520126 | 0.041671232 | 0.08006315 | 1.27065175 | 0.17418877 | 0.23258789 | 4.113750 | AT4G18470 | SNI1 |
| CHR38 | 5.888080 | 0.14225261 | 0.000000000 | 0.196289957 | 0.111538380 | 0.117268263 | 0.35260601 | 0.00000000 | 0.02464653 | 0.93446428 | 4.009014 | AT3G42670 | CHR38 |
| AT3G57800 | 7.271586 | 0.15800648 | 0.000000000 | 0.031989323 | 0.000000000 | 0.179760158 | 0.26539358 | 0.28004021 | 0.38146958 | 2.06269730 | 3.912229 | AT3G57800 | AT3G57800 |
| PIF3 | 5.404544 | 0.19783150 | 0.000000000 | 0.227173698 | 0.056111150 | 0.033283305 | 0.01478358 | 0.00000000 | 0.16805195 | 1.01379530 | 3.693513 | AT1G09530 | PIF3 |
| RING1 | 4.089627 | 0.11229920 | 0.000000000 | 0.075123208 | 0.065682752 | 0.089369208 | 0.00000000 | 0.00000000 | 0.00000000 | 0.07140771 | 3.675745 | AT5G10380 | RING1 |
| MYB55 | 6.945822 | 0.00000000 | 0.000000000 | 0.023288492 | 0.147728424 | 0.750514251 | 0.55881050 | 0.00000000 | 1.16814032 | 0.70461530 | 3.592724 | AT4G01680 | MYB55 |
| BPC7 | 4.165504 | 0.00000000 | 0.000000000 | 0.000000000 | 0.004043805 | 0.000000000 | 0.04865636 | 0.35664180 | 0.14328092 | 0.05968475 | 3.553197 | AT2G35550 | BPC7 |
| HSF A4A | 4.962823 | 0.12838948 | 0.054780610 | 0.083679895 | 0.267886536 | 0.004706377 | 0.01877069 | 0.01590173 | 0.06236722 | 0.78320502 | 3.543136 | AT4G18880 | HSF A4A |
| NAC016 | 5.829837 | 0.04516396 | 0.000000000 | 0.363094307 | 0.000000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.00000000 | 2.01853347 | 3.403045 | AT1G34180 | NAC016 |
| TGA7 | 5.128538 | 0.00000000 | 0.466028315 | 0.693673258 | 0.000000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.00000000 | 0.91988490 | 3.048951 | AT1G77920 | TGA7 |
| NAC052 | 4.412081 | 0.00000000 | 0.000000000 | 0.019616904 | 0.042285284 | 0.020685724 | 0.14517463 | 0.13746004 | 0.08378706 | 0.99213130 | 2.970940 | AT3G10490 | NAC052 |
| NAM | 3.447548 | 0.01540582 | 0.000000000 | 0.079946275 | 0.011845445 | 0.066728967 | 0.06187127 | 0.00000000 | 0.00000000 | 0.24733734 | 2.964413 | AT1G52880 | NAM |
| HSL1 | 5.621855 | 0.02080644 | 0.000000000 | 0.000000000 | 0.009556385 | 0.020678866 | 0.01677393 | 2.20276200 | 0.14400234 | 0.25660860 | 2.950667 | AT4G32010 | HSL1 |
| AT2G45120 | 4.076905 | 0.00000000 | 0.000000000 | 0.000000000 | 0.118144380 | 0.000000000 | 0.00000000 | 0.92402090 | 0.00000000 | 0.09853600 | 2.936204 | AT2G45120 | AT2G45120 |
| TRFL3 | 4.543429 | 0.19413317 | 0.005699856 | 0.525096163 | 0.110850430 | 0.062350098 | 0.00000000 | 0.00000000 | 0.01111542 | 0.71114789 | 2.923036 | AT1G17460 | TRFL3 |
| AT2G41835 | 3.547995 | 0.07794041 | 0.000000000 | 0.009931674 | 0.015927659 | 0.008243163 | 0.00000000 | 0.00000000 | 0.01146696 | 0.67095734 | 2.753528 | AT2G41835 | AT2G41835 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| WRKY14 | 3.8926763 | 0.49024458 | 0.243209359 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.74626272 | 2.4129597 | AT1G30650 | WRKY14 |
| CCA1 | 4.0251382 | 0.02223463 | 0.000000000 | 0.012925957 | 0.302568474 | 0.225524181 | 0.562408932 | 0.03407027 | 0.192127503 | 0.28173581 | 2.3915425 | AT2G46830 | CCA1 |
| AT5G25470 | 3.4725349 | 0.12612036 | 0.000000000 | 0.322128545 | 0.143933536 | 0.003503293 | 0.000000000 | 0.55786213 | 0.000000000 | 0.12606166 | 2.1929253 | AT5G25470 | AT5G25470 |
| TLP9 | 3.9610962 | 0.15882216 | 0.046468341 | 0.201633526 | 0.169958404 | 0.397056304 | 0.145446878 | 0.00000000 | 0.487262099 | 0.32539758 | 2.0290509 | AT3G06380 | TLP9 |
| AT5G62610 | 3.7998041 | 0.19236557 | 0.245521118 | 0.290300506 | 0.139158160 | 0.017692689 | 0.000000000 | 0.07981943 | 0.058001228 | 0.81755740 | 1.9593880 | AT5G62610 | AT5G62610 |
| BZIP25 | 3.4406087 | 0.38698096 | 0.139268391 | 0.019493682 | 0.102883147 | 0.000000000 | 0.014954564 | 0.18518628 | 0.089599015 | 0.56052089 | 1.9417217 | AT3G54620 | BZIP25 |
| TRB2 | 3.1279038 | 0.17205919 | 0.252754314 | 0.104866230 | 0.089088730 | 0.026402981 | 0.182139406 | 0.18495753 | 0.109344095 | 0.27419289 | 1.7320985 | AT5G67580 | TRB2 |
| RR1 | 3.0102431 | 0.47128850 | 0.208480408 | 0.317774299 | 0.102460237 | 0.000000000 | 0.110896568 | 0.02320930 | 0.053990218 | 0.18671105 | 1.5354326 | AT3G16857 | RR1 |
| AT3G19184 | 1.8877323 | 0.00000000 | 0.132870365 | 0.000000000 | 0.000000000 | 0.058846805 | 0.027726453 | 0.04307317 | 0.033017224 | 0.17793755 | 1.4142608 | AT3G19184 | AT3G19184 |
| TGA4 | 2.4635172 | 0.03491801 | 0.000000000 | 0.239695591 | 0.068029929 | 0.224467276 | 0.122772909 | 0.00000000 | 0.230944068 | 0.24805780 | 1.2946316 | AT5G10030 | TGA4 |
| AT3G08505 | 1.8564675 | 0.00000000 | 0.011501696 | 0.000000000 | 0.004043805 | 0.005931996 | 0.095862925 | 0.14042490 | 0.078295414 | 0.31757521 | 1.2028315 | AT3G08505 | AT3G08505 |
| AT2G20110 | 2.0055778 | 0.00000000 | 0.000000000 | 0.000000000 | 0.008540125 | 0.012916047 | 0.023633036 | 0.02767582 | 0.042572936 | 0.83695262 | 1.0532872 | AT2G20110 | AT2G20110 |
| E2F1 | 1.7769413 | 0.10229120 | 0.040620491 | 0.017318818 | 0.025433036 | 0.009412753 | 0.157254730 | 0.10021762 | 0.127316004 | 0.20174989 | 0.9953268 | AT5G22220 | E2F1 |
| AT5G23405 | 1.2274783 | 0.03688216 | 0.102822944 | 0.087385725 | 0.078470611 | 0.027410184 | 0.013474235 | 0.00000000 | 0.000000000 | 0.07344528 | 0.8075871 | AT5G23405 | AT5G23405 |
| NTM1 | 1.1232401 | 0.00000000 | 0.000000000 | 0.116265888 | 0.056120787 | 0.011779949 | 0.086910783 | 0.00000000 | 0.053081378 | 0.01583055 | 0.7832508 | AT4G01540 | NTM1 |
| SMZ | 0.7113781 | 0.00000000 | 0.007878508 | 0.019740125 | 0.000000000 | 0.000000000 | 0.002789573 | 0.01508810 | 0.061821504 | 0.01627648 | 0.5877838 | AT3G54990 | SMZ |
| AT5G25790 | 0.8412511 | 0.00000000 | 0.000000000 | 0.007510366 | 0.004043805 | 0.016862072 | 0.037263035 | 0.00000000 | 0.049227890 | 0.21339355 | 0.5129504 | AT5G25790 | AT5G25790 |
| AT3G54460 | 0.8041379 | 0.02541266 | 0.000000000 | 0.007387144 | 0.027640064 | 0.017683930 | 0.035262115 | 0.06489737 | 0.000000000 | 0.12985924 | 0.4959954 | AT3G54460 | AT3G54460 |
| DRIP2 | 0.9085241 | 0.02831885 | 0.055118498 | 0.000000000 | 0.008382559 | 0.035284809 | 0.091387207 | 0.04821619 | 0.017600567 | 0.16910336 | 0.4551120 | AT2G30580 | DRIP2 |
| AT4G13040 | 0.7287893 | 0.02211117 | 0.038145188 | 0.088418213 | 0.012921349 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.17759178 | 0.3896016 | AT4G13040 | AT4G13040 |
| HSFC1 | 0.3543715 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.003749446 | 0.00000000 | 0.000000000 | 0.00000000 | 0.3506221 | AT3G24520 | HSFC1 |
| TRFL1 | 0.5348390 | 0.00000000 | 0.030150697 | 0.012599425 | 0.022371326 | 0.009412753 | 0.029115651 | 0.04120750 | 0.025707464 | 0.02586074 | 0.3384135 | AT3G46590 | TRFL1 |
| AT4G36050 | 0.4075325 | 0.00000000 | 0.051229280 | 0.000000000 | 0.011828453 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.05365557 | 0.2908192 | AT4G36050 | AT4G36050 |
| AGL80 | 0.4149600 | 0.00000000 | 0.000000000 | 0.000000000 | 0.004043805 | 0.008243163 | 0.054430351 | 0.00000000 | 0.078112519 | 0.00000000 | 0.2701301 | AT5G48670 | AGL80 |
| AT2G33550 | 0.2887555 | 0.00000000 | 0.000000000 | 0.000000000 | 0.004043805 | 0.014701681 | 0.000000000 | 0.00000000 | 0.000000000 | 0.08215866 | 0.1878513 | AT2G33550 | AT2G33550 |
| MYB95 | 0.3398761 | 0.00000000 | 0.000000000 | 0.000000000 | 0.004390864 | 0.000000000 | 0.045557032 | 0.07520208 | 0.027094238 | 0.00000000 | 0.1876319 | AT1G74430 | MYB95 |
| SDG29 | 0.3117052 | 0.00000000 | 0.000000000 | 0.043036709 | 0.019010605 | 0.000000000 | 0.004970447 | 0.00965381 | 0.004877171 | 0.05605634 | 0.1741001 | AT5G53430 | SDG29 |
| FRS6 | 0.1803380 | 0.00000000 | 0.000000000 | 0.000000000 | 0.010317208 | 0.005903982 | 0.007412450 | 0.00000000 | 0.000000000 | 0.02936581 | 0.1273385 | AT1G52520 | FRS6 |
| BAM7 | 0.2274405 | 0.00000000 | 0.026406111 | 0.006993083 | 0.017390805 | 0.007045558 | 0.000000000 | 0.01162479 | 0.018654974 | 0.01307847 | 0.1262467 | AT2G45880 | BAM7 |
| AT1G05920 | 0.1496188 | 0.02983990 | 0.000000000 | 0.000000000 | 0.002195432 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.1175835 | AT1G05920 | AT1G05920 |
In [255]:
options(repr.plot.width=6, repr.plot.height=4)
ggplot(col_rank[1:10,], aes(x=reorder(GeneName, col, decreasing = FALSE), y=col)) + geom_point(size=4)+
labs(title="Columella-specific TF Prioritization",x="", y = "Combined centrality score")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [256]:
write.csv(col_rank,"Columella_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)
In [257]:
tf_rank <- col_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])
In [258]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [259]:
options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Columella ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
Add tissue level¶
In [260]:
bc_rank <- bc_rank %>% mutate(ground=cor+end, epi=atri+tri, stele=per+pro+xyl+phl, epilrc=atri+tri+lrc, rc=lrc+col)
In [261]:
head(bc_rank)
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | ground | epi | stele | epilrc | rc | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| BZIP9 | 29.19444 | 0.000000 | 0.000000 | 0.000000 | 0.01983748 | 9.05092129 | 8.8677625 | 0.05687082 | 11.19905212 | 0.00000 | 0.00000000 | AT5G24800 | 0.01983748 | 0.00000 | 29.174607 | 0.00000 | 0.00000000 |
| AT3G43430 | 34.91987 | 0.000000 | 0.000000 | 0.000000 | 0.01684128 | 12.12719380 | 9.5079047 | 4.47480476 | 8.79312886 | 0.00000 | 0.00000000 | AT3G43430 | 0.01684128 | 0.00000 | 34.903032 | 0.00000 | 0.00000000 |
| GATA2 | 32.60274 | 6.876734 | 5.844258 | 1.938035 | 2.47836267 | 0.03634172 | 0.1234438 | 0.02555554 | 0.02737594 | 11.82659 | 3.42603973 | AT2G45050 | 4.41639739 | 12.72099 | 0.212717 | 24.54758 | 15.25262898 |
| LEP | 34.89938 | 4.738138 | 5.608289 | 0.000000 | 5.48517444 | 11.78585516 | 4.1985151 | 0.14260467 | 2.92113981 | 0.00000 | 0.01966089 | AT5G13910 | 5.48517444 | 10.34643 | 19.048115 | 10.34643 | 0.01966089 |
| MYB20 | 30.03240 | 0.000000 | 0.000000 | 0.000000 | 0.31968431 | 10.63089110 | 10.9812467 | 4.08713837 | 4.01344128 | 0.00000 | 0.00000000 | AT1G66230 | 0.31968431 | 0.00000 | 29.712717 | 0.00000 | 0.00000000 |
| OBP2 | 20.54627 | 0.000000 | 0.000000 | 0.000000 | 0.00000000 | 5.48240701 | 5.4416662 | 0.00000000 | 9.62219734 | 0.00000 | 0.00000000 | AT1G07640 | 0.00000000 | 0.00000 | 20.546271 | 0.00000 | 0.00000000 |
Ground Tissue¶
In [262]:
ground_rank <- bc_rank[which(bc_rank$ground*2 > bc_rank$all),]%>% arrange(desc(ground))
ground_rank <- ground_rank[-c(match(rownames(cor_rank), rownames(ground_rank)),match(rownames(end_rank), rownames(ground_rank))),]
ground_rank$GeneName <- rownames(ground_rank)
In [263]:
ground_rank
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | ground | epi | stele | epilrc | rc | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | |
| MYB12 | 20.58663716 | 0.956650814 | 0.152088811 | 7.632693838 | 4.32108625 | 2.091767607 | 0.823233027 | 4.21278373 | 0.153946888 | 0.179971581 | 0.062414612 | AT2G47460 | 11.953780085 | 1.108739625 | 7.28173125 | 1.288711206 | 0.24238619 | MYB12 |
| HAT7 | 21.68650217 | 6.569320450 | 4.153170649 | 6.497192828 | 4.44035601 | 0.000000000 | 0.026462230 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G15150 | 10.937548841 | 10.722491099 | 0.02646223 | 10.722491099 | 0.00000000 | HAT7 |
| SOM | 15.20436442 | 1.470622061 | 0.000000000 | 2.974118902 | 6.31254015 | 3.126291892 | 1.003600964 | 0.00000000 | 0.109549976 | 0.006243032 | 0.201397440 | AT1G03790 | 9.286659050 | 1.470622061 | 4.23944283 | 1.476865093 | 0.20764047 | SOM |
| ARR3 | 8.84090365 | 1.569981724 | 0.045255081 | 3.000398755 | 1.87791425 | 0.711521340 | 0.415393560 | 0.00000000 | 0.034270536 | 0.031624113 | 1.154544284 | AT1G59940 | 4.878313010 | 1.615236806 | 1.16118544 | 1.646860919 | 1.18616840 | ARR3 |
| AN3 | 8.51330058 | 1.608248119 | 0.000000000 | 1.810969625 | 2.99898154 | 1.252108625 | 0.526894128 | 0.00000000 | 0.191231544 | 0.124867001 | 0.000000000 | AT5G28640 | 4.809951163 | 1.608248119 | 1.97023430 | 1.733115120 | 0.12486700 | AN3 |
| AT3G56230 | 9.43335659 | 1.242315834 | 0.044789643 | 2.177459518 | 2.62016038 | 0.000000000 | 0.000000000 | 1.30541492 | 0.000000000 | 1.829069983 | 0.214146309 | AT3G56230 | 4.797619896 | 1.287105477 | 1.30541492 | 3.116175460 | 2.04321629 | AT3G56230 |
| ULT1 | 7.63848239 | 1.574176264 | 0.000000000 | 2.915465274 | 1.21364407 | 1.464869415 | 0.006337322 | 0.11751876 | 0.000000000 | 0.172997668 | 0.173473621 | AT4G28190 | 4.129109344 | 1.574176264 | 1.58872550 | 1.747173932 | 0.34647129 | ULT1 |
| HSFB4 | 7.51302858 | 2.485015266 | 0.000000000 | 1.587271396 | 2.25136850 | 0.565386527 | 0.494844814 | 0.00000000 | 0.129142077 | 0.000000000 | 0.000000000 | AT1G46264 | 3.838639896 | 2.485015266 | 1.18937342 | 2.485015266 | 0.00000000 | HSFB4 |
| AT5G57150 | 5.22581608 | 0.280104880 | 0.000000000 | 0.952341853 | 2.05779348 | 0.936602708 | 0.295350991 | 0.00000000 | 0.562003303 | 0.083651300 | 0.057967564 | AT5G57150 | 3.010135337 | 0.280104880 | 1.79395700 | 0.363756180 | 0.14161886 | AT5G57150 |
| AT3G24120 | 4.02760204 | 0.987460954 | 0.000000000 | 1.747413614 | 0.88708985 | 0.290056450 | 0.063700249 | 0.00000000 | 0.018410045 | 0.029813272 | 0.003657608 | AT3G24120 | 2.634503459 | 0.987460954 | 0.37216674 | 1.017274226 | 0.03347088 | AT3G24120 |
| HB5 | 4.46468220 | 0.034499629 | 0.000000000 | 1.837895887 | 0.75492265 | 0.994181599 | 0.272756321 | 0.00000000 | 0.570426121 | 0.000000000 | 0.000000000 | AT5G65310 | 2.592818534 | 0.034499629 | 1.83736404 | 0.034499629 | 0.00000000 | HB5 |
| AT5G43530 | 4.11980528 | 1.320243160 | 0.180598980 | 1.491102987 | 0.97537700 | 0.152483159 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G43530 | 2.466479985 | 1.500842140 | 0.15248316 | 1.500842140 | 0.00000000 | AT5G43530 |
| AT3G23690 | 4.20056198 | 0.422157568 | 0.021572872 | 1.116293887 | 1.14493362 | 0.527295261 | 0.367247239 | 0.07204523 | 0.529016310 | 0.000000000 | 0.000000000 | AT3G23690 | 2.261227507 | 0.443730439 | 1.49560403 | 0.443730439 | 0.00000000 | AT3G23690 |
| AT1G16640 | 2.82602347 | 0.853422773 | 0.281267314 | 1.325190998 | 0.27905642 | 0.021333260 | 0.000000000 | 0.00000000 | 0.000000000 | 0.056543548 | 0.009209155 | AT1G16640 | 1.604247420 | 1.134690087 | 0.02133326 | 1.191233635 | 0.06575270 | AT1G16640 |
| AT4G36860 | 2.25928881 | 0.016814945 | 0.000000000 | 0.508131013 | 0.70182666 | 0.041633620 | 0.002485224 | 0.46360585 | 0.200076622 | 0.038203210 | 0.286511664 | AT4G36860 | 1.209957673 | 0.016814945 | 0.70780132 | 0.055018155 | 0.32471487 | AT4G36860 |
| COL4 | 1.74082358 | 0.087230419 | 0.034271284 | 0.842739119 | 0.33166002 | 0.132001751 | 0.030297868 | 0.00000000 | 0.000000000 | 0.158715801 | 0.123907319 | AT5G24930 | 1.174399136 | 0.121501703 | 0.16229962 | 0.280217504 | 0.28262312 | COL4 |
| ZFN1 | 2.19685703 | 0.000000000 | 0.000000000 | 0.872589048 | 0.26813672 | 0.305104390 | 0.060433511 | 0.00000000 | 0.151919739 | 0.283332948 | 0.255340666 | AT3G02830 | 1.140725772 | 0.000000000 | 0.51745764 | 0.283332948 | 0.53867361 | ZFN1 |
| ERF15 | 1.04268670 | 0.000000000 | 0.000000000 | 0.453298035 | 0.42838781 | 0.000000000 | 0.000000000 | 0.16100085 | 0.000000000 | 0.000000000 | 0.000000000 | AT2G31230 | 0.881685848 | 0.000000000 | 0.16100085 | 0.000000000 | 0.00000000 | ERF15 |
| AT3G20800 | 0.68090580 | 0.028624368 | 0.000000000 | 0.057470848 | 0.31253828 | 0.032900169 | 0.015043661 | 0.03096379 | 0.069347966 | 0.063989574 | 0.070027151 | AT3G20800 | 0.370009128 | 0.028624368 | 0.14825558 | 0.092613942 | 0.13401672 | AT3G20800 |
| AT1G60700 | 0.69383352 | 0.015887070 | 0.163642782 | 0.192254935 | 0.16203105 | 0.033410643 | 0.000000000 | 0.06246783 | 0.000000000 | 0.036902550 | 0.027236652 | AT1G60700 | 0.354285986 | 0.179529852 | 0.09587848 | 0.216432402 | 0.06413920 | AT1G60700 |
| AT1G62120 | 0.57892859 | 0.025276741 | 0.046763936 | 0.109328580 | 0.21600643 | 0.097085320 | 0.000000000 | 0.06132875 | 0.000000000 | 0.023138833 | 0.000000000 | AT1G62120 | 0.325335008 | 0.072040677 | 0.15841407 | 0.095179510 | 0.02313883 | AT1G62120 |
| ZFN3 | 0.47482583 | 0.012665567 | 0.115253787 | 0.219319768 | 0.08763971 | 0.000000000 | 0.000000000 | 0.00000000 | 0.039946993 | 0.000000000 | 0.000000000 | AT5G16540 | 0.306959480 | 0.127919355 | 0.03994699 | 0.127919355 | 0.00000000 | ZFN3 |
| NSI | 0.45209141 | 0.009803481 | 0.119791447 | 0.145021753 | 0.11770703 | 0.007050551 | 0.044091530 | 0.00000000 | 0.008625619 | 0.000000000 | 0.000000000 | AT1G32070 | 0.262728781 | 0.129594928 | 0.05976770 | 0.129594928 | 0.00000000 | NSI |
| NBS1 | 0.32881085 | 0.059811480 | 0.008893077 | 0.088469838 | 0.08398144 | 0.028312454 | 0.000000000 | 0.01760467 | 0.000000000 | 0.023059029 | 0.018678852 | AT3G02680 | 0.172451281 | 0.068704557 | 0.04591713 | 0.091763586 | 0.04173788 | NBS1 |
| AGL67 | 0.26459351 | 0.103454382 | 0.000000000 | 0.132046430 | 0.02909270 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G77950 | 0.161139127 | 0.103454382 | 0.00000000 | 0.103454382 | 0.00000000 | AGL67 |
| AT5G08520 | 0.01570086 | 0.000000000 | 0.005994526 | 0.004667502 | 0.00503883 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G08520 | 0.009706332 | 0.005994526 | 0.00000000 | 0.005994526 | 0.00000000 | AT5G08520 |
In [264]:
options(repr.plot.width=6, repr.plot.height=4)
ggplot(ground_rank[1:10,], aes(x=reorder(GeneName, ground, decreasing = FALSE), y=ground)) + geom_point(size=4)+
labs(title="Ground Tissue-specific TF Prioritization",x="", y = "Combined centrality score")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [265]:
write.csv(ground_rank,"Ground_Tissue_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)
In [266]:
tf_rank <- ground_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])
In [267]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [268]:
options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Ground Tissue ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
Epidermis¶
In [269]:
epi_rank <- bc_rank[which(bc_rank$epi*2 > bc_rank$all),]%>% arrange(desc(epi))
epi_rank <- epi_rank[-c(match(rownames(atri_rank), rownames(epi_rank)),match(rownames(tri_rank), rownames(epi_rank))),]
epi_rank$GeneName <- rownames(epi_rank)
In [270]:
epi_rank
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | ground | epi | stele | epilrc | rc | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | |
| WRKY75 | 17.3137141 | 5.48442845 | 6.37997623 | 1.094587355 | 0.005012539 | 0.000000000 | 0.000000000 | 0.01547734 | 0.000000000 | 0.00000000 | 4.334232198 | AT5G13080 | 1.09959989 | 11.8644047 | 0.015477341 | 11.8644047 | 4.33423220 | WRKY75 |
| KDR | 13.2479881 | 4.77198535 | 6.47536038 | 1.767663806 | 0.232978578 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | AT1G26945 | 2.00064238 | 11.2473457 | 0.000000000 | 11.2473457 | 0.00000000 | KDR |
| ATS | 15.5726439 | 3.98176392 | 4.46301144 | 1.260592779 | 1.768128428 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 3.77163292 | 0.327514456 | AT5G42630 | 3.02872121 | 8.4447754 | 0.000000000 | 12.2164083 | 4.09914738 | ATS |
| IAA14 | 12.6469056 | 4.34804559 | 3.18817695 | 2.176120062 | 0.000000000 | 0.708571499 | 0.152808737 | 0.07436741 | 0.007428925 | 1.96815612 | 0.023230326 | AT4G14550 | 2.17612006 | 7.5362225 | 0.943176568 | 9.5043787 | 1.99138645 | IAA14 |
| ARR5 | 9.0789020 | 4.49052784 | 2.27129374 | 0.385290112 | 0.089170205 | 0.005098190 | 0.221600554 | 0.24092141 | 0.138903720 | 0.78827153 | 0.447824687 | AT3G48100 | 0.47446032 | 6.7618216 | 0.606523871 | 7.5500931 | 1.23609622 | ARR5 |
| TGA10 | 11.1820639 | 5.47581644 | 1.18839943 | 2.826826390 | 1.675499784 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.01204013 | 0.003481673 | AT5G06839 | 4.50232617 | 6.6642159 | 0.000000000 | 6.6762560 | 0.01552181 | TGA10 |
| AT2G37120 | 8.7008660 | 3.05738235 | 3.08094433 | 0.756358586 | 0.249492802 | 0.490249603 | 0.193194649 | 0.72332523 | 0.138036975 | 0.01188147 | 0.000000000 | AT2G37120 | 1.00585139 | 6.1383267 | 1.544806460 | 6.1502082 | 0.01188147 | AT2G37120 |
| WRKY72 | 11.4985672 | 1.50070259 | 4.63247532 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 3.49531485 | 1.870074408 | AT5G15130 | 0.00000000 | 6.1331779 | 0.000000000 | 9.6284928 | 5.36538926 | WRKY72 |
| ATHB13 | 10.9507213 | 2.03069623 | 3.75438011 | 1.708549990 | 2.010370947 | 1.005427385 | 0.267747951 | 0.00000000 | 0.052354396 | 0.00000000 | 0.121194254 | AT1G69780 | 3.71892094 | 5.7850763 | 1.325529732 | 5.7850763 | 0.12119425 | ATHB13 |
| EGL3 | 8.5557791 | 1.79214386 | 3.80237377 | 1.590448457 | 1.370813044 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | AT1G63650 | 2.96126150 | 5.5945176 | 0.000000000 | 5.5945176 | 0.00000000 | EGL3 |
| WRI1 | 8.3070863 | 2.40817786 | 3.17443163 | 1.457208586 | 0.183973314 | 0.015608069 | 0.000000000 | 0.00000000 | 0.000000000 | 0.96528676 | 0.102400073 | AT3G54320 | 1.64118190 | 5.5826095 | 0.015608069 | 6.5478962 | 1.06768683 | WRI1 |
| OFP15 | 8.6619134 | 3.69726381 | 1.57803779 | 0.883692353 | 1.126956540 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 1.26111476 | 0.114848112 | AT2G36050 | 2.01064889 | 5.2753016 | 0.000000000 | 6.5364164 | 1.37596287 | OFP15 |
| NFL | 8.7845136 | 2.23040739 | 2.67939890 | 0.981956173 | 2.001087234 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.55378107 | 0.337882823 | AT5G65640 | 2.98304341 | 4.9098063 | 0.000000000 | 5.4635874 | 0.89166389 | NFL |
| CRF4 | 5.8159044 | 2.82433655 | 1.79017778 | 0.406860345 | 0.440787945 | 0.034378538 | 0.000000000 | 0.03959160 | 0.000000000 | 0.17724536 | 0.102526300 | AT4G27950 | 0.84764829 | 4.6145143 | 0.073970134 | 4.7917597 | 0.27977166 | CRF4 |
| HDG7 | 6.6513690 | 2.24826852 | 1.29119419 | 1.907911916 | 0.889834312 | 0.064258495 | 0.000000000 | 0.01557628 | 0.000000000 | 0.23432527 | 0.000000000 | AT5G52170 | 2.79774623 | 3.5394627 | 0.079834772 | 3.7737880 | 0.23432527 | HDG7 |
| AT5G61590 | 6.6446914 | 3.01560456 | 0.35893041 | 0.407535275 | 0.241671053 | 2.253745846 | 0.146280039 | 0.04487157 | 0.176052685 | 0.00000000 | 0.000000000 | AT5G61590 | 0.64920633 | 3.3745350 | 2.620950142 | 3.3745350 | 0.00000000 | AT5G61590 |
| NAC044 | 4.3642739 | 1.75388550 | 1.33213510 | 0.340029522 | 0.872765209 | 0.008914984 | 0.000000000 | 0.00000000 | 0.000000000 | 0.05654355 | 0.000000000 | AT3G01600 | 1.21279473 | 3.0860206 | 0.008914984 | 3.1425642 | 0.05654355 | NAC044 |
| AT4G26810 | 5.7392670 | 1.31895084 | 1.72625204 | 0.747009274 | 1.096791909 | 0.656435380 | 0.085773445 | 0.04761815 | 0.024581361 | 0.00000000 | 0.035854578 | AT4G26810 | 1.84380118 | 3.0452029 | 0.814408332 | 3.0452029 | 0.03585458 | AT4G26810 |
| WRKY27 | 5.2816360 | 1.73682626 | 1.30710173 | 0.082367583 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 2.04055144 | 0.114788991 | AT5G52830 | 0.08236758 | 3.0439280 | 0.000000000 | 5.0844794 | 2.15534043 | WRKY27 |
| AT1G76110 | 2.9000699 | 1.15081042 | 1.06702920 | 0.264391680 | 0.254828822 | 0.086565750 | 0.000000000 | 0.01753383 | 0.000000000 | 0.03252536 | 0.026384881 | AT1G76110 | 0.51922050 | 2.2178396 | 0.104099578 | 2.2503650 | 0.05891025 | AT1G76110 |
| ORC1B | 3.3561607 | 1.17928753 | 0.91247290 | 0.549922557 | 0.125430178 | 0.347515580 | 0.047791933 | 0.14719599 | 0.000000000 | 0.01186190 | 0.034682139 | AT4G12620 | 0.67535274 | 2.0917604 | 0.542503499 | 2.1036223 | 0.04654403 | ORC1B |
| HMGB2 | 3.9552633 | 1.28241616 | 0.73436573 | 0.944499302 | 0.066631680 | 0.319875528 | 0.224235784 | 0.02966602 | 0.222696390 | 0.13087667 | 0.000000000 | AT1G20693 | 1.01113098 | 2.0167819 | 0.796473724 | 2.1476586 | 0.13087667 | HMGB2 |
| AT2G36026 | 2.8077842 | 0.69317394 | 1.30222348 | 0.104491770 | 0.059292612 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.51752201 | 0.131080424 | AT2G36026 | 0.16378438 | 1.9953974 | 0.000000000 | 2.5129194 | 0.64860243 | AT2G36026 |
| AT5G23930 | 3.8409993 | 1.08734477 | 0.88930445 | 1.321592042 | 0.131827898 | 0.014147144 | 0.000000000 | 0.00000000 | 0.000000000 | 0.39678296 | 0.000000000 | AT5G23930 | 1.45341994 | 1.9766492 | 0.014147144 | 2.3734322 | 0.39678296 | AT5G23930 |
| HRS1 | 3.4812234 | 1.45954038 | 0.47970991 | 0.300906842 | 1.164178265 | 0.041800725 | 0.035087322 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | AT1G13300 | 1.46508511 | 1.9392503 | 0.076888047 | 1.9392503 | 0.00000000 | HRS1 |
| ERF104 | 2.8439780 | 0.90820245 | 0.84004746 | 0.077193067 | 0.100651160 | 0.446663651 | 0.148989889 | 0.02063198 | 0.000000000 | 0.16540846 | 0.136189831 | AT5G61600 | 0.17784423 | 1.7482499 | 0.616285520 | 1.9136584 | 0.30159829 | ERF104 |
| HB16 | 3.1813217 | 0.79176600 | 0.91851772 | 1.049127531 | 0.211826348 | 0.047668533 | 0.065568923 | 0.00000000 | 0.023313911 | 0.05938806 | 0.014144674 | AT4G40060 | 1.26095388 | 1.7102837 | 0.136551367 | 1.7696718 | 0.07353273 | HB16 |
| NAC053 | 2.7458545 | 1.16828731 | 0.38821319 | 0.152464561 | 0.039999473 | 0.000000000 | 0.033614609 | 0.20793386 | 0.000000000 | 0.31583429 | 0.439507238 | AT3G10500 | 0.19246403 | 1.5565005 | 0.241548465 | 1.8723348 | 0.75534153 | NAC053 |
| NF-YB11 | 2.7942300 | 1.35223619 | 0.20190300 | 0.365370890 | 0.102500499 | 0.066046520 | 0.091824618 | 0.17889293 | 0.026163639 | 0.24042141 | 0.168870323 | AT2G27470 | 0.46787139 | 1.5541392 | 0.362927711 | 1.7945606 | 0.40929173 | NF-YB11 |
| AT3G09735 | 2.9516495 | 0.46214259 | 1.05998572 | 0.004667502 | 0.224341925 | 0.259780651 | 0.204837570 | 0.49794283 | 0.237950682 | 0.00000000 | 0.000000000 | AT3G09735 | 0.22900943 | 1.5221283 | 1.200511728 | 1.5221283 | 0.00000000 | AT3G09735 |
| KAN | 2.7076342 | 1.14288549 | 0.37247758 | 0.061533811 | 0.032199836 | 0.031148618 | 0.000000000 | 0.00000000 | 0.000000000 | 0.83435108 | 0.233037755 | AT5G16560 | 0.09373365 | 1.5153631 | 0.031148618 | 2.3497141 | 1.06738883 | KAN |
| AT1G21150 | 2.6339002 | 0.28989747 | 1.20271850 | 0.747717948 | 0.111522264 | 0.086300936 | 0.000000000 | 0.11202734 | 0.014761850 | 0.06895389 | 0.000000000 | AT1G21150 | 0.85924021 | 1.4926160 | 0.213090124 | 1.5615699 | 0.06895389 | AT1G21150 |
| AT5G11340 | 2.2623709 | 0.53121872 | 0.84237611 | 0.042750348 | 0.051522399 | 0.218766732 | 0.247348989 | 0.09762606 | 0.215146792 | 0.00000000 | 0.015614790 | AT5G11340 | 0.09427275 | 1.3735948 | 0.778888574 | 1.3735948 | 0.01561479 | AT5G11340 |
| AT1G61990 | 1.8454066 | 0.83659082 | 0.33934770 | 0.394398057 | 0.124568180 | 0.102524932 | 0.000000000 | 0.00000000 | 0.000000000 | 0.04797695 | 0.000000000 | AT1G61990 | 0.51896624 | 1.1759385 | 0.102524932 | 1.2239155 | 0.04797695 | AT1G61990 |
| MBD6 | 1.3256667 | 0.64957950 | 0.39220977 | 0.077670409 | 0.068749500 | 0.023873768 | 0.000000000 | 0.06862540 | 0.002758878 | 0.02339034 | 0.018809123 | AT5G59380 | 0.14641991 | 1.0417893 | 0.095258045 | 1.0651796 | 0.04219947 | MBD6 |
| HFR1 | 1.6832700 | 0.61128058 | 0.41170358 | 0.590624163 | 0.069661723 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | AT1G02340 | 0.66028589 | 1.0229842 | 0.000000000 | 1.0229842 | 0.00000000 | HFR1 |
| NF-YC10 | 1.3163525 | 0.48627728 | 0.35841820 | 0.121083105 | 0.193216785 | 0.022806749 | 0.000000000 | 0.01418868 | 0.034402462 | 0.02752479 | 0.058434472 | AT1G07980 | 0.31429989 | 0.8446955 | 0.071397892 | 0.8722203 | 0.08595926 | NF-YC10 |
| PTAC15 | 1.3447889 | 0.55005885 | 0.12794613 | 0.252039655 | 0.113653861 | 0.096995207 | 0.053670027 | 0.00965381 | 0.057809209 | 0.08296212 | 0.000000000 | AT5G54180 | 0.36569352 | 0.6780050 | 0.218128253 | 0.7609671 | 0.08296212 | PTAC15 |
| AT3G46950 | 0.9206607 | 0.18833790 | 0.43466410 | 0.090054310 | 0.089060202 | 0.014119130 | 0.015041006 | 0.00000000 | 0.000000000 | 0.03269618 | 0.056687911 | AT3G46950 | 0.17911451 | 0.6230020 | 0.029160136 | 0.6556982 | 0.08938409 | AT3G46950 |
| AT5G28300 | 0.9416232 | 0.22606934 | 0.34002829 | 0.318517546 | 0.011544687 | 0.000000000 | 0.000000000 | 0.00000000 | 0.045463365 | 0.00000000 | 0.000000000 | AT5G28300 | 0.33006223 | 0.5660976 | 0.045463365 | 0.5660976 | 0.00000000 | AT5G28300 |
| ATMAK3 | 0.8317792 | 0.29560798 | 0.21599825 | 0.000000000 | 0.053477224 | 0.034229751 | 0.063746355 | 0.05750740 | 0.015544854 | 0.09039036 | 0.005277005 | AT2G38130 | 0.05347722 | 0.5116062 | 0.171028364 | 0.6019966 | 0.09566737 | ATMAK3 |
| NPR4 | 0.8017285 | 0.08735866 | 0.31395421 | 0.009318665 | 0.020372821 | 0.003564800 | 0.093413238 | 0.00000000 | 0.000000000 | 0.09551591 | 0.178230237 | AT4G19660 | 0.02969149 | 0.4013129 | 0.096978038 | 0.4968288 | 0.27374615 | NPR4 |
| KAPP | 0.6519213 | 0.07180732 | 0.30190602 | 0.000000000 | 0.028992640 | 0.000000000 | 0.000000000 | 0.09871044 | 0.016955291 | 0.07175872 | 0.061790883 | AT5G19280 | 0.02899264 | 0.3737133 | 0.115665726 | 0.4454721 | 0.13354960 | KAPP |
| AT5G64950 | 0.5340296 | 0.09681475 | 0.25127237 | 0.000000000 | 0.140181319 | 0.009412753 | 0.000000000 | 0.03634843 | 0.000000000 | 0.00000000 | 0.000000000 | AT5G64950 | 0.14018132 | 0.3480871 | 0.045761184 | 0.3480871 | 0.00000000 | AT5G64950 |
| AT3G24490 | 0.6360101 | 0.10999868 | 0.23445933 | 0.012925957 | 0.050182458 | 0.026700131 | 0.040252850 | 0.07881678 | 0.064816760 | 0.01785714 | 0.000000000 | AT3G24490 | 0.06310841 | 0.3444580 | 0.210586520 | 0.3623151 | 0.01785714 | AT3G24490 |
| BEH4 | 0.5159049 | 0.23838133 | 0.08952304 | 0.021011927 | 0.016752323 | 0.000000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.09733626 | 0.052900032 | AT1G78700 | 0.03776425 | 0.3279044 | 0.000000000 | 0.4252406 | 0.15023630 | BEH4 |
| ETR1 | 0.6335790 | 0.23281957 | 0.09452031 | 0.076095432 | 0.062478923 | 0.000000000 | 0.013793661 | 0.01033613 | 0.061825859 | 0.05328957 | 0.028419578 | AT1G66340 | 0.13857436 | 0.3273399 | 0.085955645 | 0.3806295 | 0.08170915 | ETR1 |
| AT3G53440 | 0.5768835 | 0.12493991 | 0.17725974 | 0.014897510 | 0.019685153 | 0.014326674 | 0.007455671 | 0.00000000 | 0.004877171 | 0.10045668 | 0.112985009 | AT3G53440 | 0.03458266 | 0.3021997 | 0.026659516 | 0.4026563 | 0.21344169 | AT3G53440 |
| AT3G24820 | 0.5183494 | 0.06032566 | 0.23371446 | 0.011676924 | 0.104270728 | 0.014057623 | 0.010087322 | 0.02385259 | 0.011119736 | 0.00000000 | 0.049244346 | AT3G24820 | 0.11594765 | 0.2940401 | 0.059117274 | 0.2940401 | 0.04924435 | AT3G24820 |
| AT2G23060 | 0.5052637 | 0.21549678 | 0.07845829 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.10537828 | 0.000000000 | 0.00000000 | 0.105930335 | AT2G23060 | 0.00000000 | 0.2939551 | 0.105378281 | 0.2939551 | 0.10593034 | AT2G23060 |
| SDG40 | 0.4111201 | 0.18312740 | 0.11001343 | 0.012925957 | 0.015426277 | 0.000000000 | 0.000000000 | 0.03756320 | 0.009558839 | 0.04250503 | 0.000000000 | AT5G17240 | 0.02835223 | 0.2931408 | 0.047122037 | 0.3356459 | 0.04250503 | SDG40 |
| IPT2 | 0.4558322 | 0.05663770 | 0.17806463 | 0.046053935 | 0.012405129 | 0.004734391 | 0.022512887 | 0.04166672 | 0.077981033 | 0.01577579 | 0.000000000 | AT2G27760 | 0.05845906 | 0.2347023 | 0.146895027 | 0.2504781 | 0.01577579 | IPT2 |
| AT2G46040 | 0.3834989 | 0.12283173 | 0.10051270 | 0.000000000 | 0.028553057 | 0.025536019 | 0.000000000 | 0.03966728 | 0.005601980 | 0.06079610 | 0.000000000 | AT2G46040 | 0.02855306 | 0.2233444 | 0.070805281 | 0.2841405 | 0.06079610 | AT2G46040 |
| FRF1 | 0.1991305 | 0.08474725 | 0.04946627 | 0.012476203 | 0.021619802 | 0.007045558 | 0.010875396 | 0.01290002 | 0.000000000 | 0.00000000 | 0.000000000 | AT3G59470 | 0.03409600 | 0.1342135 | 0.030820975 | 0.1342135 | 0.00000000 | FRF1 |
In [271]:
options(repr.plot.width=6, repr.plot.height=4)
ggplot(epi_rank[1:10,], aes(x=reorder(GeneName, epi, decreasing = FALSE), y=epi)) + geom_point(size=4)+
labs(title="Epidermis-specific TF Prioritization",x="", y = "Combined centrality score")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [272]:
write.csv(epi_rank,"Epidermis_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)
In [273]:
tf_rank <- epi_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])
In [274]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [275]:
options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Epidermis ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
Epidermis + LRC¶
In [276]:
epilrc_rank <- bc_rank[which(bc_rank$epilrc*2 > bc_rank$all),]%>% arrange(desc(epilrc))
epilrc_rank <- epilrc_rank[-c(match(rownames(atri_rank), rownames(epilrc_rank)),match(rownames(tri_rank), rownames(epilrc_rank)),match(rownames(lrc_rank), rownames(epilrc_rank))),]
epilrc_rank$GeneName <- rownames(epilrc_rank)
In [277]:
epilrc_rank
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | ground | epi | stele | epilrc | rc | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | |
| GATA2 | 32.60274 | 6.8767338 | 5.8442579 | 1.9380347 | 2.478362673 | 0.03634172 | 0.12344377 | 0.02555554 | 0.027375945 | 11.826589 | 3.42603973 | AT2G45050 | 4.4163974 | 12.7209917 | 0.21271697 | 24.547581 | 15.252629 | GATA2 |
| BT2 | 24.62512 | 3.6324323 | 1.1102322 | 0.2931351 | 0.029871855 | 0.00000000 | 0.00000000 | 0.02836394 | 0.000000000 | 11.334815 | 8.19626518 | AT3G48360 | 0.3230069 | 4.7426645 | 0.02836394 | 16.077479 | 19.531080 | BT2 |
| WER | 21.56819 | 4.2519322 | 2.6099449 | 1.5113967 | 2.401269110 | 0.11513015 | 0.00000000 | 0.00000000 | 0.000000000 | 8.516907 | 2.16161412 | AT5G14750 | 3.9126658 | 6.8618771 | 0.11513015 | 15.378784 | 10.678521 | WER |
| NAI1 | 23.01590 | 3.5954146 | 0.9426660 | 0.2911109 | 0.099846879 | 0.00000000 | 0.00000000 | 0.03480724 | 0.000000000 | 9.114967 | 8.93708230 | AT2G22770 | 0.3909578 | 4.5380806 | 0.03480724 | 13.653048 | 18.052049 | NAI1 |
| CRF3 | 21.97712 | 2.3588840 | 2.3367416 | 0.5017725 | 1.740671622 | 1.00570468 | 0.10547801 | 0.00000000 | 0.120318333 | 8.713660 | 5.09389294 | AT5G53290 | 2.2424441 | 4.6956256 | 1.23150102 | 13.409286 | 13.807553 | CRF3 |
| WRKY17 | 18.32451 | 3.3287603 | 2.8638731 | 1.5999619 | 0.911767702 | 0.09181357 | 0.10100814 | 1.36508204 | 0.174774268 | 7.192677 | 0.69479190 | AT2G24570 | 2.5117296 | 6.1926334 | 1.73267802 | 13.385311 | 7.887469 | WRKY17 |
| CRF2 | 24.48926 | 2.8611439 | 1.7348995 | 2.3187351 | 1.991696826 | 1.61858152 | 0.09880570 | 1.16485839 | 1.533178032 | 8.344904 | 2.82245557 | AT4G23750 | 4.3104319 | 4.5960434 | 4.41542364 | 12.940947 | 11.167360 | CRF2 |
| ATS | 15.57264 | 3.9817639 | 4.4630114 | 1.2605928 | 1.768128428 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 3.771633 | 0.32751446 | AT5G42630 | 3.0287212 | 8.4447754 | 0.00000000 | 12.216408 | 4.099147 | ATS |
| AT1G36060 | 23.06258 | 4.4237248 | 1.2039815 | 1.6503947 | 0.173598751 | 0.00000000 | 0.00000000 | 0.06677439 | 0.000000000 | 6.478396 | 9.06570848 | AT1G36060 | 1.8239934 | 5.6277063 | 0.06677439 | 12.106102 | 15.544104 | AT1G36060 |
| WRKY9 | 13.85294 | 3.6295556 | 3.0432671 | 0.0000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 5.227408 | 1.95271264 | AT1G68150 | 0.0000000 | 6.6728227 | 0.00000000 | 11.900231 | 7.180121 | WRKY9 |
| WRKY75 | 17.31371 | 5.4844285 | 6.3799762 | 1.0945874 | 0.005012539 | 0.00000000 | 0.00000000 | 0.01547734 | 0.000000000 | 0.000000 | 4.33423220 | AT5G13080 | 1.0995999 | 11.8644047 | 0.01547734 | 11.864405 | 4.334232 | WRKY75 |
| BRON | 19.43391 | 3.1949996 | 0.1294355 | 1.3676938 | 1.561107290 | 0.03950412 | 0.00000000 | 0.00000000 | 0.000000000 | 8.324952 | 4.81622044 | AT1G75710 | 2.9288011 | 3.3244352 | 0.03950412 | 11.649387 | 13.141172 | BRON |
| SMB | 20.51296 | 2.6041856 | 0.0000000 | 1.6886152 | 0.214020406 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 8.761212 | 7.24493062 | AT1G79580 | 1.9026356 | 2.6041856 | 0.00000000 | 11.365398 | 16.006143 | SMB |
| KDR | 13.24799 | 4.7719853 | 6.4753604 | 1.7676638 | 0.232978578 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.000000 | 0.00000000 | AT1G26945 | 2.0006424 | 11.2473457 | 0.00000000 | 11.247346 | 0.000000 | KDR |
| IAA1 | 13.99014 | 3.4484378 | 1.0372984 | 0.3589142 | 0.000000000 | 0.00000000 | 0.00000000 | 0.17173008 | 0.000000000 | 6.512181 | 2.46158019 | AT4G14560 | 0.3589142 | 4.4857362 | 0.17173008 | 10.997917 | 8.973761 | IAA1 |
| ATCTH | 20.18172 | 3.1944095 | 2.5668273 | 0.1332551 | 0.846391333 | 0.04457492 | 0.66404843 | 3.43485948 | 0.262589493 | 5.199889 | 3.83487626 | AT2G25900 | 0.9796464 | 5.7612368 | 4.40607233 | 10.961126 | 9.034765 | ATCTH |
| GATA4 | 17.27459 | 3.2425888 | 4.9107818 | 3.2613821 | 3.067715109 | 0.08091410 | 0.06357851 | 0.13541446 | 0.000000000 | 2.512218 | 0.00000000 | AT3G60530 | 6.3290973 | 8.1533706 | 0.27990707 | 10.665589 | 2.512218 | GATA4 |
| TMO7 | 17.42119 | 4.3741465 | 3.0642120 | 2.8561333 | 1.622301563 | 0.00000000 | 0.00000000 | 0.00000000 | 0.023581642 | 3.219309 | 2.26150501 | AT1G74500 | 4.4784349 | 7.4383585 | 0.02358164 | 10.657667 | 5.480814 | TMO7 |
| GATA17L | 19.00534 | 3.4903475 | 2.4856195 | 1.8540587 | 1.565943112 | 2.83520352 | 0.07369229 | 0.17507039 | 0.025902967 | 4.200371 | 2.29913248 | AT4G16141 | 3.4200018 | 5.9759670 | 3.10986916 | 10.176338 | 6.499503 | GATA17L |
| AT1G22190 | 15.63742 | 3.8996098 | 0.1212121 | 0.7737265 | 0.138885864 | 0.00000000 | 0.05192027 | 0.28787218 | 0.060517590 | 5.741278 | 4.56240213 | AT1G22190 | 0.9126123 | 4.0208220 | 0.40031004 | 9.762100 | 10.303680 | AT1G22190 |
| WRKY72 | 11.49857 | 1.5007026 | 4.6324753 | 0.0000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 3.495315 | 1.87007441 | AT5G15130 | 0.0000000 | 6.1331779 | 0.00000000 | 9.628493 | 5.365389 | WRKY72 |
| IAA14 | 12.64691 | 4.3480456 | 3.1881770 | 2.1761201 | 0.000000000 | 0.70857150 | 0.15280874 | 0.07436741 | 0.007428925 | 1.968156 | 0.02323033 | AT4G14550 | 2.1761201 | 7.5362225 | 0.94317657 | 9.504379 | 1.991386 | IAA14 |
| EEL | 15.39312 | 1.8150391 | 0.0000000 | 1.9964973 | 0.256296526 | 0.18440473 | 0.06875881 | 0.15174492 | 0.000000000 | 7.133394 | 3.78698434 | AT2G41070 | 2.2527938 | 1.8150391 | 0.40490846 | 8.948434 | 10.920379 | EEL |
| RITF1 | 17.30592 | 3.4790908 | 1.8650326 | 2.3749576 | 1.853643089 | 0.82622737 | 0.00000000 | 0.12419772 | 0.000000000 | 3.527153 | 3.25561831 | AT2G12646 | 4.2286006 | 5.3441234 | 0.95042510 | 8.871276 | 6.782771 | RITF1 |
| 3xHMG-box2 | 13.79692 | 2.5558244 | 0.0000000 | 1.4079683 | 1.759433504 | 1.17552880 | 0.02838558 | 0.15740650 | 0.150451205 | 6.212588 | 0.34933662 | AT4G23800 | 3.1674018 | 2.5558244 | 1.51177208 | 8.768413 | 6.561925 | 3xHMG-box2 |
| GATA17 | 16.67635 | 2.8369700 | 2.9789887 | 1.8842903 | 1.937457634 | 1.66579930 | 0.10575911 | 1.07209165 | 0.000000000 | 2.932746 | 1.26225145 | AT3G16870 | 3.8217479 | 5.8159587 | 2.84365006 | 8.748704 | 4.194997 | GATA17 |
| AT2G35605 | 14.51961 | 2.2243351 | 3.4348289 | 1.9626006 | 1.827765158 | 0.99613068 | 0.08830108 | 0.39996749 | 0.031971410 | 2.550186 | 1.00352555 | AT2G35605 | 3.7903658 | 5.6591640 | 1.51637066 | 8.209350 | 3.553712 | AT2G35605 |
| WRKY11 | 12.76437 | 1.9219201 | 1.7906380 | 0.1496153 | 1.066641632 | 0.09569928 | 0.13187675 | 1.43331813 | 0.100382145 | 4.268116 | 1.80616522 | AT4G31550 | 1.2162570 | 3.7125580 | 1.76127631 | 7.980674 | 6.074281 | WRKY11 |
| AIL6 | 15.27031 | 0.4115598 | 0.0000000 | 0.2941365 | 0.178942531 | 0.07861256 | 0.00000000 | 0.05208743 | 0.000000000 | 7.417725 | 6.83724606 | AT5G10510 | 0.4730790 | 0.4115598 | 0.13069999 | 7.829285 | 14.254971 | AIL6 |
| AT2G46160 | 11.64507 | 2.5811600 | 2.2692677 | 1.5989502 | 1.235662501 | 0.02742674 | 0.10097295 | 0.25837471 | 0.057222697 | 2.832556 | 0.68347271 | AT2G46160 | 2.8346127 | 4.8504277 | 0.44399709 | 7.682983 | 3.516028 | AT2G46160 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| NPR4 | 0.8017285 | 0.087358663 | 0.31395421 | 0.009318665 | 0.020372821 | 0.003564800 | 0.093413238 | 0.000000000 | 0.000000000 | 0.09551591 | 0.178230237 | AT4G19660 | 0.02969149 | 0.40131287 | 0.09697804 | 0.49682878 | 0.27374615 | NPR4 |
| NPR1 | 0.7156180 | 0.146888195 | 0.21082686 | 0.084877719 | 0.030352653 | 0.000000000 | 0.012426119 | 0.000000000 | 0.000000000 | 0.13553859 | 0.094707862 | AT1G64280 | 0.11523037 | 0.35771506 | 0.01242612 | 0.49325365 | 0.23024646 | NPR1 |
| E2F3 | 0.8359657 | 0.190213492 | 0.00000000 | 0.089397588 | 0.060979961 | 0.073262688 | 0.037235135 | 0.000000000 | 0.009548257 | 0.25551871 | 0.119809907 | AT2G36010 | 0.15037755 | 0.19021349 | 0.12004608 | 0.44573220 | 0.37532862 | E2F3 |
| KAPP | 0.6519213 | 0.071807317 | 0.30190602 | 0.000000000 | 0.028992640 | 0.000000000 | 0.000000000 | 0.098710435 | 0.016955291 | 0.07175872 | 0.061790883 | AT5G19280 | 0.02899264 | 0.37371334 | 0.11566573 | 0.44547205 | 0.13354960 | KAPP |
| TIFY8 | 0.8444050 | 0.104186926 | 0.00000000 | 0.000000000 | 0.183782237 | 0.160322480 | 0.000000000 | 0.000000000 | 0.046993932 | 0.33272534 | 0.016394100 | AT4G32570 | 0.18378224 | 0.10418693 | 0.20731641 | 0.43691226 | 0.34911944 | TIFY8 |
| BEH4 | 0.5159049 | 0.238381327 | 0.08952304 | 0.021011927 | 0.016752323 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.09733626 | 0.052900032 | AT1G78700 | 0.03776425 | 0.32790437 | 0.00000000 | 0.42524063 | 0.15023630 | BEH4 |
| AT4G14490 | 0.6834523 | 0.251316820 | 0.05186001 | 0.053601026 | 0.071276953 | 0.020706259 | 0.000000000 | 0.116481958 | 0.000000000 | 0.11820926 | 0.000000000 | AT4G14490 | 0.12487798 | 0.30317683 | 0.13718822 | 0.42138609 | 0.11820926 | AT4G14490 |
| AT3G53440 | 0.5768835 | 0.124939913 | 0.17725974 | 0.014897510 | 0.019685153 | 0.014326674 | 0.007455671 | 0.000000000 | 0.004877171 | 0.10045668 | 0.112985009 | AT3G53440 | 0.03458266 | 0.30219965 | 0.02665952 | 0.40265633 | 0.21344169 | AT3G53440 |
| ETR1 | 0.6335790 | 0.232819574 | 0.09452031 | 0.076095432 | 0.062478923 | 0.000000000 | 0.013793661 | 0.010336125 | 0.061825859 | 0.05328957 | 0.028419578 | AT1G66340 | 0.13857436 | 0.32733988 | 0.08595565 | 0.38062946 | 0.08170915 | ETR1 |
| AT3G24490 | 0.6360101 | 0.109998678 | 0.23445933 | 0.012925957 | 0.050182458 | 0.026700131 | 0.040252850 | 0.078816778 | 0.064816760 | 0.01785714 | 0.000000000 | AT3G24490 | 0.06310841 | 0.34445800 | 0.21058652 | 0.36231515 | 0.01785714 | AT3G24490 |
| TAF6B | 0.5441624 | 0.014025828 | 0.24312279 | 0.000000000 | 0.012852015 | 0.026372455 | 0.000000000 | 0.000000000 | 0.055564961 | 0.10253592 | 0.089688456 | AT1G54360 | 0.01285202 | 0.25714862 | 0.08193742 | 0.35968454 | 0.19222437 | TAF6B |
| BEH1 | 0.5819530 | 0.173861315 | 0.07153026 | 0.129686963 | 0.004001335 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.10340623 | 0.099466891 | AT3G50750 | 0.13368830 | 0.24539157 | 0.00000000 | 0.34879780 | 0.20287312 | BEH1 |
| AT5G64950 | 0.5340296 | 0.096814753 | 0.25127237 | 0.000000000 | 0.140181319 | 0.009412753 | 0.000000000 | 0.036348430 | 0.000000000 | 0.00000000 | 0.000000000 | AT5G64950 | 0.14018132 | 0.34808712 | 0.04576118 | 0.34808712 | 0.00000000 | AT5G64950 |
| AT5G07810 | 0.6737721 | 0.211904783 | 0.07459588 | 0.257425147 | 0.063181256 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.06124380 | 0.005421276 | AT5G07810 | 0.32060640 | 0.28650066 | 0.00000000 | 0.34774447 | 0.06666508 | AT5G07810 |
| SDG40 | 0.4111201 | 0.183127401 | 0.11001343 | 0.012925957 | 0.015426277 | 0.000000000 | 0.000000000 | 0.037563198 | 0.009558839 | 0.04250503 | 0.000000000 | AT5G17240 | 0.02835223 | 0.29314083 | 0.04712204 | 0.33564587 | 0.04250503 | SDG40 |
| ASIL2 | 0.6649379 | 0.110421542 | 0.11989358 | 0.036322712 | 0.032694733 | 0.007073572 | 0.011161896 | 0.040339753 | 0.026479506 | 0.10416010 | 0.176390549 | AT3G14180 | 0.06901745 | 0.23031512 | 0.08505473 | 0.33447522 | 0.28055065 | ASIL2 |
| AT3G24820 | 0.5183494 | 0.060325660 | 0.23371446 | 0.011676924 | 0.104270728 | 0.014057623 | 0.010087322 | 0.023852593 | 0.011119736 | 0.00000000 | 0.049244346 | AT3G24820 | 0.11594765 | 0.29404012 | 0.05911727 | 0.29404012 | 0.04924435 | AT3G24820 |
| AT2G23060 | 0.5052637 | 0.215496779 | 0.07845829 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.105378281 | 0.000000000 | 0.00000000 | 0.105930335 | AT2G23060 | 0.00000000 | 0.29395507 | 0.10537828 | 0.29395507 | 0.10593034 | AT2G23060 |
| AT2G46040 | 0.3834989 | 0.122831729 | 0.10051270 | 0.000000000 | 0.028553057 | 0.025536019 | 0.000000000 | 0.039667282 | 0.005601980 | 0.06079610 | 0.000000000 | AT2G46040 | 0.02855306 | 0.22334443 | 0.07080528 | 0.28414053 | 0.06079610 | AT2G46040 |
| AT5G41580 | 0.4184683 | 0.042952453 | 0.05613883 | 0.030032002 | 0.022114786 | 0.011751935 | 0.000000000 | 0.000000000 | 0.026072789 | 0.15317763 | 0.076227836 | AT5G41580 | 0.05214679 | 0.09909128 | 0.03782472 | 0.25226891 | 0.22940547 | AT5G41580 |
| IPT2 | 0.4558322 | 0.056637699 | 0.17806463 | 0.046053935 | 0.012405129 | 0.004734391 | 0.022512887 | 0.041666717 | 0.077981033 | 0.01577579 | 0.000000000 | AT2G27760 | 0.05845906 | 0.23470233 | 0.14689503 | 0.25047812 | 0.01577579 | IPT2 |
| AT1G74410 | 0.4558504 | 0.057174866 | 0.13064273 | 0.007791657 | 0.026449297 | 0.002367195 | 0.043296881 | 0.057233419 | 0.004877171 | 0.06149650 | 0.064520712 | AT1G74410 | 0.03424095 | 0.18781760 | 0.10777467 | 0.24931410 | 0.12601722 | AT1G74410 |
| AT1G77250 | 0.3912126 | 0.046872544 | 0.02707408 | 0.011693263 | 0.026660878 | 0.002367195 | 0.075634217 | 0.010974984 | 0.006394282 | 0.15138419 | 0.032156955 | AT1G77250 | 0.03835414 | 0.07394662 | 0.09537068 | 0.22533081 | 0.18354114 | AT1G77250 |
| PCFS4 | 0.4104091 | 0.057745571 | 0.00000000 | 0.000000000 | 0.041329477 | 0.016542354 | 0.064835893 | 0.021427898 | 0.047561190 | 0.16096671 | 0.000000000 | AT4G04885 | 0.04132948 | 0.05774557 | 0.15036734 | 0.21871228 | 0.16096671 | PCFS4 |
| TAFII21 | 0.3788948 | 0.063086635 | 0.00000000 | 0.000000000 | 0.024649593 | 0.017644405 | 0.012599004 | 0.011611362 | 0.069197713 | 0.15250601 | 0.027600122 | AT1G54140 | 0.02464959 | 0.06308663 | 0.11105248 | 0.21559265 | 0.18010613 | TAFII21 |
| MYB1 | 0.2398095 | 0.096400827 | 0.00000000 | 0.000000000 | 0.011507976 | 0.008215149 | 0.023849883 | 0.020654510 | 0.000000000 | 0.06410415 | 0.015077036 | AT3G09230 | 0.01150798 | 0.09640083 | 0.05271954 | 0.16050498 | 0.07918119 | MYB1 |
| RR14 | 0.1877025 | 0.021325098 | 0.07081564 | 0.000000000 | 0.000000000 | 0.005411690 | 0.012339676 | 0.033744815 | 0.000000000 | 0.04406558 | 0.000000000 | AT2G01760 | 0.00000000 | 0.09214074 | 0.05149618 | 0.13620632 | 0.04406558 | RR14 |
| FRF1 | 0.1991305 | 0.084747246 | 0.04946627 | 0.012476203 | 0.021619802 | 0.007045558 | 0.010875396 | 0.012900021 | 0.000000000 | 0.00000000 | 0.000000000 | AT3G59470 | 0.03409600 | 0.13421352 | 0.03082098 | 0.13421352 | 0.00000000 | FRF1 |
| AT3G04450 | 0.1511582 | 0.004281654 | 0.02490514 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.06528348 | 0.056687911 | AT3G04450 | 0.00000000 | 0.02918679 | 0.00000000 | 0.09447027 | 0.12197139 | AT3G04450 |
| AT5G47790 | 0.1567419 | 0.021689778 | 0.04387311 | 0.000000000 | 0.038885919 | 0.007073572 | 0.006293661 | 0.006456723 | 0.000000000 | 0.02842052 | 0.004048649 | AT5G47790 | 0.03888592 | 0.06556289 | 0.01982396 | 0.09398341 | 0.03246917 | AT5G47790 |
In [278]:
options(repr.plot.width=8, repr.plot.height=40)
ggplot(epilrc_rank, aes(x=reorder(GeneName, epilrc, decreasing = FALSE), y=epilrc)) + geom_point(size=4)+
labs(title="Epidermis+LRC-specific TF Prioritization",x="", y = "Combined centrality score (betweeness, out and in degree)")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [279]:
write.csv(epilrc_rank,"Epidermis_LRC_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)
In [280]:
tf_rank <- epilrc_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])
In [281]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [282]:
options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Epidermis (includes LRC)", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
Stele¶
In [283]:
stele_rank <- bc_rank[which(bc_rank$stele*2 > bc_rank$all),]%>% arrange(desc(stele))
stele_rank <- stele_rank[-c(match(rownames(per_rank), rownames(stele_rank)),match(rownames(pro_rank), rownames(stele_rank)),match(rownames(xyl_rank), rownames(stele_rank)),match(rownames(phl_rank), rownames(stele_rank))),]
stele_rank$GeneName <- rownames(stele_rank)
In [284]:
stele_rank
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | ground | epi | stele | epilrc | rc | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | |
| AT3G43430 | 34.91987 | 0.00000000 | 0.000000000 | 0.00000000 | 0.016841283 | 12.12719380 | 9.507905 | 4.47480476 | 8.7931289 | 0.000000000 | 0.00000000 | AT3G43430 | 0.016841283 | 0.00000000 | 34.90303 | 0.000000000 | 0.00000000 | AT3G43430 |
| MYB20 | 30.03240 | 0.00000000 | 0.000000000 | 0.00000000 | 0.319684315 | 10.63089110 | 10.981247 | 4.08713837 | 4.0134413 | 0.000000000 | 0.00000000 | AT1G66230 | 0.319684315 | 0.00000000 | 29.71272 | 0.000000000 | 0.00000000 | MYB20 |
| BZIP9 | 29.19444 | 0.00000000 | 0.000000000 | 0.00000000 | 0.019837480 | 9.05092129 | 8.867763 | 0.05687082 | 11.1990521 | 0.000000000 | 0.00000000 | AT5G24800 | 0.019837480 | 0.00000000 | 29.17461 | 0.000000000 | 0.00000000 | BZIP9 |
| AT1G29160 | 26.54555 | 0.01902631 | 0.000000000 | 0.00000000 | 0.000000000 | 6.79408747 | 7.762429 | 0.01525082 | 7.8463015 | 3.056753223 | 1.05170007 | AT1G29160 | 0.000000000 | 0.01902631 | 22.41807 | 3.075779533 | 4.10845329 | AT1G29160 |
| AT3G60490 | 21.85674 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 8.08244563 | 5.554085 | 0.14141893 | 8.0292191 | 0.000000000 | 0.04957311 | AT3G60490 | 0.000000000 | 0.00000000 | 21.80717 | 0.000000000 | 0.04957311 | AT3G60490 |
| AT2G34140 | 32.72309 | 0.04724803 | 0.000000000 | 0.25521355 | 1.135486766 | 8.75993314 | 6.877722 | 0.42124328 | 5.3896828 | 3.379574589 | 6.45698638 | AT2G34140 | 1.390700317 | 0.04724803 | 21.44858 | 3.426822616 | 9.83656097 | AT2G34140 |
| MYB43 | 21.35556 | 0.00000000 | 0.000000000 | 0.01182391 | 0.003006310 | 8.23756692 | 9.810498 | 0.19019593 | 3.1024648 | 0.000000000 | 0.00000000 | AT5G16600 | 0.014830225 | 0.00000000 | 21.34073 | 0.000000000 | 0.00000000 | MYB43 |
| HAT2 | 32.68893 | 0.23599606 | 0.000000000 | 3.08490829 | 8.045560446 | 8.80783792 | 4.077204 | 3.65164416 | 4.7228737 | 0.000000000 | 0.06290083 | AT5G47370 | 11.130468731 | 0.23599606 | 21.25956 | 0.235996065 | 0.06290083 | HAT2 |
| BT1 | 21.06198 | 0.00000000 | 0.000000000 | 0.18553998 | 0.275729783 | 5.21780895 | 6.907699 | 6.05818042 | 2.4170262 | 0.000000000 | 0.00000000 | AT5G63160 | 0.461269760 | 0.00000000 | 20.60071 | 0.000000000 | 0.00000000 | BT1 |
| OBP2 | 20.54627 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 5.48240701 | 5.441666 | 0.00000000 | 9.6221973 | 0.000000000 | 0.00000000 | AT1G07640 | 0.000000000 | 0.00000000 | 20.54627 | 0.000000000 | 0.00000000 | OBP2 |
| AT5G05790 | 20.41074 | 0.00000000 | 0.000000000 | 0.00000000 | 0.145528063 | 10.14826356 | 2.782122 | 0.19008703 | 7.1447356 | 0.000000000 | 0.00000000 | AT5G05790 | 0.145528063 | 0.00000000 | 20.26521 | 0.000000000 | 0.00000000 | AT5G05790 |
| HB-8 | 20.70821 | 0.00000000 | 0.000000000 | 0.00000000 | 0.071413411 | 0.28929886 | 7.655046 | 7.46358740 | 4.2592937 | 0.000000000 | 0.96956766 | AT4G32880 | 0.071413411 | 0.00000000 | 19.66723 | 0.000000000 | 0.96956766 | HB-8 |
| DAG1 | 19.12421 | 0.00000000 | 0.000000000 | 0.00000000 | 0.006218002 | 7.03655820 | 5.967543 | 1.45403275 | 4.6598594 | 0.000000000 | 0.00000000 | AT3G61850 | 0.006218002 | 0.00000000 | 19.11799 | 0.000000000 | 0.00000000 | DAG1 |
| LEP | 34.89938 | 4.73813773 | 5.608288956 | 0.00000000 | 5.485174436 | 11.78585516 | 4.198515 | 0.14260467 | 2.9211398 | 0.000000000 | 0.01966089 | AT5G13910 | 5.485174436 | 10.34642669 | 19.04811 | 10.346426687 | 0.01966089 | LEP |
| AT5G51780 | 22.40588 | 0.37454899 | 0.227158239 | 0.45293594 | 2.411834952 | 6.86298227 | 6.304918 | 2.55997040 | 3.2115262 | 0.000000000 | 0.00000000 | AT5G51780 | 2.864770897 | 0.60170723 | 18.93940 | 0.601707229 | 0.00000000 | AT5G51780 |
| TMO6 | 20.08671 | 0.00000000 | 0.000000000 | 0.00000000 | 1.485898656 | 5.76737678 | 5.426185 | 1.34690659 | 6.0603438 | 0.000000000 | 0.00000000 | AT5G60200 | 1.485898656 | 0.00000000 | 18.60081 | 0.000000000 | 0.00000000 | TMO6 |
| GBF6 | 20.37023 | 0.00000000 | 0.000000000 | 0.00000000 | 1.452010760 | 1.53958777 | 6.222675 | 1.95853546 | 8.8174277 | 0.017143586 | 0.36284715 | AT4G34590 | 1.452010760 | 0.00000000 | 18.53823 | 0.017143586 | 0.37999074 | GBF6 |
| MIF1 | 24.16585 | 0.07128522 | 0.000000000 | 0.18671725 | 3.999123918 | 8.88658200 | 5.162602 | 0.00000000 | 4.4307380 | 0.125214771 | 1.30358794 | AT1G74660 | 4.185841166 | 0.07128522 | 18.47992 | 0.196499994 | 1.42880271 | MIF1 |
| ATAUX2-11 | 21.07583 | 0.19340544 | 0.000000000 | 0.44825862 | 1.543580233 | 2.89993813 | 5.355602 | 7.94507347 | 1.8131299 | 0.521689937 | 0.35514871 | AT5G43700 | 1.991838853 | 0.19340544 | 18.01374 | 0.715095373 | 0.87683865 | ATAUX2-11 |
| AT2G41130 | 18.61575 | 0.00000000 | 0.000000000 | 0.00000000 | 1.383295295 | 7.92902790 | 4.588704 | 3.87678089 | 0.8379461 | 0.000000000 | 0.00000000 | AT2G41130 | 1.383295295 | 0.00000000 | 17.23246 | 0.000000000 | 0.00000000 | AT2G41130 |
| AT3G11280 | 16.04817 | 0.00000000 | 0.000000000 | 0.00000000 | 0.009554638 | 6.08858168 | 3.542393 | 0.25573934 | 6.1519048 | 0.000000000 | 0.00000000 | AT3G11280 | 0.009554638 | 0.00000000 | 16.03862 | 0.000000000 | 0.00000000 | AT3G11280 |
| HB-7 | 19.40749 | 0.03979606 | 0.058310207 | 0.60365194 | 2.545569057 | 5.05704243 | 5.921552 | 1.07773851 | 3.9071319 | 0.098996656 | 0.09770080 | AT2G46680 | 3.149221002 | 0.09810627 | 15.96346 | 0.197102921 | 0.19669745 | HB-7 |
| OBP3 | 15.91579 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 5.08201268 | 6.141362 | 0.00000000 | 4.6924140 | 0.000000000 | 0.00000000 | AT3G55370 | 0.000000000 | 0.00000000 | 15.91579 | 0.000000000 | 0.00000000 | OBP3 |
| IAA13 | 16.10234 | 0.03646135 | 0.009397079 | 0.09726509 | 0.241823206 | 2.99051483 | 5.175564 | 2.38858390 | 5.1627273 | 0.000000000 | 0.00000000 | AT2G33310 | 0.339088299 | 0.04585843 | 15.71739 | 0.045858426 | 0.00000000 | IAA13 |
| AT1G69580 | 15.62139 | 0.00000000 | 0.000000000 | 0.00000000 | 0.085626577 | 2.02178383 | 3.749156 | 2.86734166 | 6.8974839 | 0.000000000 | 0.00000000 | AT1G69580 | 0.085626577 | 0.00000000 | 15.53577 | 0.000000000 | 0.00000000 | AT1G69580 |
| ATHB-15 | 15.03417 | 0.00000000 | 0.000000000 | 0.00000000 | 0.000000000 | 0.03150225 | 6.380496 | 7.07116671 | 1.5510058 | 0.000000000 | 0.00000000 | AT1G52150 | 0.000000000 | 0.00000000 | 15.03417 | 0.000000000 | 0.00000000 | ATHB-15 |
| IAA11 | 15.03123 | 0.00000000 | 0.084106863 | 0.05002803 | 0.108662695 | 2.93012090 | 2.760059 | 3.95039434 | 4.8778354 | 0.014930955 | 0.25509492 | AT4G28640 | 0.158690728 | 0.08410686 | 14.51841 | 0.099037818 | 0.27002587 | IAA11 |
| UNE12 | 15.02764 | 0.00000000 | 0.000000000 | 0.00000000 | 0.539538894 | 4.61197071 | 3.276522 | 3.89687891 | 2.6720078 | 0.006243032 | 0.02448246 | AT4G02590 | 0.539538894 | 0.00000000 | 14.45738 | 0.006243032 | 0.03072549 | UNE12 |
| NF-YB2 | 17.89090 | 0.15568155 | 0.337876116 | 0.31123412 | 1.129316445 | 3.01883199 | 3.127118 | 6.53336700 | 1.6563471 | 0.425489440 | 1.19564026 | AT5G47640 | 1.440550561 | 0.49355766 | 14.33566 | 0.919047103 | 1.62112970 | NF-YB2 |
| bZIP44 | 14.49052 | 0.13565473 | 0.000000000 | 0.02561407 | 0.238377502 | 6.43444893 | 1.606523 | 0.03520935 | 5.8898247 | 0.000000000 | 0.12486973 | AT1G75390 | 0.263991575 | 0.13565473 | 13.96601 | 0.135654731 | 0.12486973 | bZIP44 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| AT5G40710 | 0.32074714 | 0.077552708 | 0.000000000 | 0.000000000 | 0.026343882 | 0.025436619 | 0.130915690 | 0.032291355 | 0.028206883 | 0.000000000 | 0.000000000 | AT5G40710 | 0.026343882 | 0.077552708 | 0.21685055 | 0.077552708 | 0.000000000 | AT5G40710 |
| AT1G76870 | 0.21645476 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.023515196 | 0.088432835 | 0.086971330 | 0.017535399 | 0.000000000 | 0.000000000 | AT1G76870 | 0.000000000 | 0.000000000 | 0.21645476 | 0.000000000 | 0.000000000 | AT1G76870 |
| FRS4 | 0.28179240 | 0.000000000 | 0.023978104 | 0.000000000 | 0.024621056 | 0.030197946 | 0.094173789 | 0.003879403 | 0.087661697 | 0.003772636 | 0.013507772 | AT1G76320 | 0.024621056 | 0.023978104 | 0.21591283 | 0.027750740 | 0.017280407 | FRS4 |
| AT2G04740 | 0.38186100 | 0.000000000 | 0.097255931 | 0.012654929 | 0.017216298 | 0.102805560 | 0.000000000 | 0.000000000 | 0.106206041 | 0.009867664 | 0.035854578 | AT2G04740 | 0.029871227 | 0.097255931 | 0.20901160 | 0.107123594 | 0.045722242 | AT2G04740 |
| TAF12 | 0.29198797 | 0.018234488 | 0.000000000 | 0.000000000 | 0.017179223 | 0.046789725 | 0.035711584 | 0.121916247 | 0.003229725 | 0.005533199 | 0.043393778 | AT3G10070 | 0.017179223 | 0.018234488 | 0.20764728 | 0.023767687 | 0.048926977 | TAF12 |
| TRFL5 | 0.28843276 | 0.000000000 | 0.023385830 | 0.000000000 | 0.003006310 | 0.037204532 | 0.027337461 | 0.069605068 | 0.066913133 | 0.050007600 | 0.010972823 | AT1G15720 | 0.003006310 | 0.023385830 | 0.20106019 | 0.073393430 | 0.060980423 | TRFL5 |
| U2AF35B | 0.37576546 | 0.075097591 | 0.067815718 | 0.000000000 | 0.012503001 | 0.082328071 | 0.005788074 | 0.036409450 | 0.076210039 | 0.019613513 | 0.000000000 | AT5G42820 | 0.012503001 | 0.142913309 | 0.20073563 | 0.162526822 | 0.019613513 | U2AF35B |
| SWI2 | 0.30555727 | 0.000000000 | 0.000000000 | 0.007387144 | 0.033997878 | 0.022552460 | 0.073768205 | 0.014188681 | 0.083459917 | 0.013295340 | 0.056907648 | AT1G03750 | 0.041385022 | 0.000000000 | 0.19396926 | 0.013295340 | 0.070202988 | SWI2 |
| SDG25 | 0.26150189 | 0.000000000 | 0.012049062 | 0.000000000 | 0.024642291 | 0.000000000 | 0.054214443 | 0.006456723 | 0.129787352 | 0.000000000 | 0.034352018 | AT5G42400 | 0.024642291 | 0.012049062 | 0.19045852 | 0.012049062 | 0.034352018 | SDG25 |
| AT2G24650 | 0.33673852 | 0.000000000 | 0.000000000 | 0.014774289 | 0.014493050 | 0.019374565 | 0.072562265 | 0.011844934 | 0.082390570 | 0.048716182 | 0.072582665 | AT2G24650 | 0.029267339 | 0.000000000 | 0.18617233 | 0.048716182 | 0.121298846 | AT2G24650 |
| MAF3 | 0.33163631 | 0.006215890 | 0.000000000 | 0.019863347 | 0.060639626 | 0.026313416 | 0.106784893 | 0.049675471 | 0.000000000 | 0.009141583 | 0.053002081 | AT5G65060 | 0.080502973 | 0.006215890 | 0.18277378 | 0.015357473 | 0.062143664 | MAF3 |
| AT5G46915 | 0.17556557 | 0.000000000 | 0.000000000 | 0.000000000 | 0.002011285 | 0.085885865 | 0.007455671 | 0.080212749 | 0.000000000 | 0.000000000 | 0.000000000 | AT5G46915 | 0.002011285 | 0.000000000 | 0.17355429 | 0.000000000 | 0.000000000 | AT5G46915 |
| TRB3 | 0.30047903 | 0.006726558 | 0.058734740 | 0.000000000 | 0.028727564 | 0.010582344 | 0.087562871 | 0.013370487 | 0.059343305 | 0.000000000 | 0.035431160 | AT3G49850 | 0.028727564 | 0.065461298 | 0.17085901 | 0.065461298 | 0.035431160 | TRB3 |
| TGA6 | 0.21873577 | 0.000000000 | 0.000000000 | 0.000000000 | 0.030024740 | 0.017739959 | 0.067472783 | 0.042830614 | 0.029739131 | 0.000000000 | 0.030928548 | AT3G12250 | 0.030024740 | 0.000000000 | 0.15778249 | 0.000000000 | 0.030928548 | TGA6 |
| LDL3 | 0.29544958 | 0.011829953 | 0.000000000 | 0.012476203 | 0.019206348 | 0.014091116 | 0.113829606 | 0.022729981 | 0.005601980 | 0.021036995 | 0.074647400 | AT4G16310 | 0.031682550 | 0.011829953 | 0.15625268 | 0.032866948 | 0.095684395 | LDL3 |
| AT2G24690 | 0.25979631 | 0.030391522 | 0.014604840 | 0.035253744 | 0.024917157 | 0.021158170 | 0.108239838 | 0.003879403 | 0.021351636 | 0.000000000 | 0.000000000 | AT2G24690 | 0.060170901 | 0.044996362 | 0.15462905 | 0.044996362 | 0.000000000 | AT2G24690 |
| HAC4 | 0.24127760 | 0.000000000 | 0.090748757 | 0.000000000 | 0.002011285 | 0.066166892 | 0.000000000 | 0.082350671 | 0.000000000 | 0.000000000 | 0.000000000 | AT1G55970 | 0.002011285 | 0.090748757 | 0.14851756 | 0.090748757 | 0.000000000 | HAC4 |
| AT1G19860 | 0.24923329 | 0.076688891 | 0.000000000 | 0.000000000 | 0.021379442 | 0.053599016 | 0.063065102 | 0.021972900 | 0.006994738 | 0.005533199 | 0.000000000 | AT1G19860 | 0.021379442 | 0.076688891 | 0.14563176 | 0.082222091 | 0.005533199 | AT1G19860 |
| AT3G08020 | 0.24925131 | 0.000000000 | 0.034828643 | 0.012476203 | 0.029357735 | 0.011751935 | 0.060709023 | 0.000000000 | 0.072968232 | 0.000000000 | 0.027159540 | AT3G08020 | 0.041833938 | 0.034828643 | 0.14542919 | 0.034828643 | 0.027159540 | AT3G08020 |
| AT4G08455 | 0.28551717 | 0.000000000 | 0.026679230 | 0.000000000 | 0.039993083 | 0.022272772 | 0.049509187 | 0.025866600 | 0.046537756 | 0.000000000 | 0.074658545 | AT4G08455 | 0.039993083 | 0.026679230 | 0.14418631 | 0.026679230 | 0.074658545 | AT4G08455 |
| AT1G55750 | 0.19224525 | 0.000000000 | 0.017786153 | 0.000000000 | 0.024646657 | 0.037125396 | 0.026642950 | 0.019158238 | 0.059592091 | 0.007293763 | 0.000000000 | AT1G55750 | 0.024646657 | 0.017786153 | 0.14251867 | 0.025079916 | 0.007293763 | AT1G55750 |
| AT2G17150 | 0.18460590 | 0.000000000 | 0.000000000 | 0.010054895 | 0.019783364 | 0.002367195 | 0.086255456 | 0.000000000 | 0.051089087 | 0.015055903 | 0.000000000 | AT2G17150 | 0.029838260 | 0.000000000 | 0.13971174 | 0.015055903 | 0.015055903 | AT2G17150 |
| MYB64 | 0.21013465 | 0.028821923 | 0.048195024 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.073530712 | 0.059586991 | 0.000000000 | 0.000000000 | AT5G11050 | 0.000000000 | 0.077016948 | 0.13311770 | 0.077016948 | 0.000000000 | MYB64 |
| EMB93 | 0.15615541 | 0.000000000 | 0.000000000 | 0.000000000 | 0.017575996 | 0.045349543 | 0.010875396 | 0.065548273 | 0.006394282 | 0.010411917 | 0.000000000 | AT2G03050 | 0.017575996 | 0.000000000 | 0.12816749 | 0.010411917 | 0.010411917 | EMB93 |
| SDIR1 | 0.21710980 | 0.000000000 | 0.000000000 | 0.000000000 | 0.099051004 | 0.013651677 | 0.000000000 | 0.086270418 | 0.018136701 | 0.000000000 | 0.000000000 | AT3G55530 | 0.099051004 | 0.000000000 | 0.11805880 | 0.000000000 | 0.000000000 | SDIR1 |
| TCP9 | 0.16331901 | 0.000000000 | 0.009090501 | 0.000000000 | 0.041222498 | 0.054357569 | 0.036316682 | 0.005922467 | 0.016409297 | 0.000000000 | 0.000000000 | AT2G45680 | 0.041222498 | 0.009090501 | 0.11300601 | 0.009090501 | 0.000000000 | TCP9 |
| AT4G19650 | 0.10231615 | 0.000000000 | 0.009090501 | 0.000000000 | 0.005038830 | 0.018161605 | 0.049045350 | 0.007625412 | 0.004812003 | 0.000000000 | 0.008542453 | AT4G19650 | 0.005038830 | 0.009090501 | 0.07964437 | 0.009090501 | 0.008542453 | AT4G19650 |
| AT4G11400 | 0.10756337 | 0.000000000 | 0.038319705 | 0.000000000 | 0.000000000 | 0.025356859 | 0.000000000 | 0.043886803 | 0.000000000 | 0.000000000 | 0.000000000 | AT4G11400 | 0.000000000 | 0.038319705 | 0.06924366 | 0.038319705 | 0.000000000 | AT4G11400 |
| AT3G51180 | 0.08759731 | 0.015405815 | 0.000000000 | 0.000000000 | 0.021961514 | 0.003536786 | 0.027510346 | 0.000000000 | 0.019182846 | 0.000000000 | 0.000000000 | AT3G51180 | 0.021961514 | 0.015405815 | 0.05022998 | 0.015405815 | 0.000000000 | AT3G51180 |
| NF-YA6 | 0.06068834 | 0.000000000 | 0.000000000 | 0.000000000 | 0.023582798 | 0.011723921 | 0.000000000 | 0.000000000 | 0.025381624 | 0.000000000 | 0.000000000 | AT3G14020 | 0.023582798 | 0.000000000 | 0.03710554 | 0.000000000 | 0.000000000 | NF-YA6 |
In [285]:
options(repr.plot.width=6, repr.plot.height=4)
ggplot(stele_rank[1:10,], aes(x=reorder(GeneName, stele, decreasing = FALSE), y=stele)) + geom_point(size=4)+
labs(title="Stele-specific TF Prioritization",x="", y = "Combined centrality score")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [286]:
write.csv(stele_rank,"Stele_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)
In [287]:
tf_rank <- stele_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])
In [288]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [289]:
options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Stele", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
Root Cap¶
In [290]:
rc_rank <- bc_rank[which(bc_rank$rc*2 > bc_rank$all),]%>% arrange(desc(rc))
rc_rank <- rc_rank[-c(match(rownames(lrc_rank), rownames(rc_rank)),match(rownames(col_rank), rownames(rc_rank))),]
rc_rank$GeneName <- rownames(rc_rank)
In [291]:
rc_rank
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | ground | epi | stele | epilrc | rc | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | |
| BT2 | 24.62512 | 3.63243231 | 1.11023217 | 0.29313509 | 0.02987186 | 0.00000000 | 0.00000000 | 0.02836394 | 0.000000000 | 11.334815 | 8.196265 | AT3G48360 | 0.32300695 | 4.74266448 | 0.02836394 | 16.077479 | 19.531080 | BT2 |
| NAI1 | 23.01590 | 3.59541458 | 0.94266601 | 0.29111093 | 0.09984688 | 0.00000000 | 0.00000000 | 0.03480724 | 0.000000000 | 9.114967 | 8.937082 | AT2G22770 | 0.39095781 | 4.53808059 | 0.03480724 | 13.653048 | 18.052049 | NAI1 |
| LBD15 | 19.76810 | 0.07609731 | 0.00000000 | 0.00000000 | 0.00000000 | 0.00000000 | 0.00000000 | 2.40501651 | 0.000000000 | 9.390180 | 7.896803 | AT2G40470 | 0.00000000 | 0.07609731 | 2.40501651 | 9.466278 | 17.286983 | LBD15 |
| SMB | 20.51296 | 2.60418556 | 0.00000000 | 1.68861519 | 0.21402041 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 8.761212 | 7.244931 | AT1G79580 | 1.90263560 | 2.60418556 | 0.00000000 | 11.365398 | 16.006143 | SMB |
| AT1G36060 | 23.06258 | 4.42372479 | 1.20398150 | 1.65039467 | 0.17359875 | 0.00000000 | 0.00000000 | 0.06677439 | 0.000000000 | 6.478396 | 9.065708 | AT1G36060 | 1.82399342 | 5.62770628 | 0.06677439 | 12.106102 | 15.544104 | AT1G36060 |
| AIL6 | 15.27031 | 0.41155982 | 0.00000000 | 0.29413650 | 0.17894253 | 0.07861256 | 0.00000000 | 0.05208743 | 0.000000000 | 7.417725 | 6.837246 | AT5G10510 | 0.47307903 | 0.41155982 | 0.13069999 | 7.829285 | 14.254971 | AIL6 |
| CRF3 | 21.97712 | 2.35888401 | 2.33674161 | 0.50177245 | 1.74067162 | 1.00570468 | 0.10547801 | 0.00000000 | 0.120318333 | 8.713660 | 5.093893 | AT5G53290 | 2.24244407 | 4.69562562 | 1.23150102 | 13.409286 | 13.807553 | CRF3 |
| BRON | 19.43391 | 3.19499965 | 0.12943552 | 1.36769378 | 1.56110729 | 0.03950412 | 0.00000000 | 0.00000000 | 0.000000000 | 8.324952 | 4.816220 | AT1G75710 | 2.92880107 | 3.32443516 | 0.03950412 | 11.649387 | 13.141172 | BRON |
| PLT1 | 22.45757 | 3.59503298 | 0.00000000 | 2.35070851 | 2.47832175 | 0.15184049 | 0.05937524 | 0.86518002 | 0.000000000 | 6.550585 | 6.406522 | AT3G20840 | 4.82903026 | 3.59503298 | 1.07639575 | 10.145618 | 12.957106 | PLT1 |
| AT1G26680 | 15.49236 | 0.05400877 | 0.00000000 | 0.99504250 | 1.53634929 | 0.07189208 | 0.00000000 | 0.00000000 | 0.000000000 | 5.968598 | 6.866465 | AT1G26680 | 2.53139179 | 0.05400877 | 0.07189208 | 6.022607 | 12.835063 | AT1G26680 |
| ZFP5 | 23.60082 | 2.60660283 | 0.09155688 | 0.02023301 | 0.00000000 | 0.00000000 | 2.74638834 | 4.84269017 | 0.944938866 | 5.417175 | 6.931232 | AT1G10480 | 0.02023301 | 2.69815971 | 8.53401737 | 8.115335 | 12.348407 | ZFP5 |
| AT1G32700 | 19.46024 | 0.00000000 | 0.00000000 | 0.00000000 | 0.08488294 | 1.37084567 | 1.79691876 | 1.54955888 | 2.941386341 | 3.439036 | 8.277616 | AT1G32700 | 0.08488294 | 0.00000000 | 7.65870965 | 3.439036 | 11.716652 | AT1G32700 |
| NAC094 | 15.74732 | 1.88038954 | 0.00000000 | 1.56546165 | 1.19488706 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 5.291631 | 5.814952 | AT5G39820 | 2.76034871 | 1.88038954 | 0.00000000 | 7.172020 | 11.106582 | NAC094 |
| EEL | 15.39312 | 1.81503913 | 0.00000000 | 1.99649730 | 0.25629653 | 0.18440473 | 0.06875881 | 0.15174492 | 0.000000000 | 7.133394 | 3.786984 | AT2G41070 | 2.25279383 | 1.81503913 | 0.40490846 | 8.948434 | 10.920379 | EEL |
| PLT2 | 17.03674 | 1.89290722 | 0.44054300 | 2.23299921 | 2.04107694 | 0.01592157 | 0.00000000 | 0.02506736 | 0.000000000 | 4.121808 | 6.266412 | AT1G51190 | 4.27407615 | 2.33345021 | 0.04098893 | 6.455259 | 10.388220 | PLT2 |
| AT1G22190 | 15.63742 | 3.89960985 | 0.12121212 | 0.77372647 | 0.13888586 | 0.00000000 | 0.05192027 | 0.28787218 | 0.060517590 | 5.741278 | 4.562402 | AT1G22190 | 0.91261233 | 4.02082197 | 0.40031004 | 9.762100 | 10.303680 | AT1G22190 |
| GATA9 | 16.97801 | 1.63907316 | 0.02511891 | 0.72781165 | 0.49645267 | 0.11856715 | 0.04010268 | 3.82489668 | 0.064742300 | 4.216369 | 5.824879 | AT4G32890 | 1.22426432 | 1.66419207 | 4.04830882 | 5.880561 | 10.041248 | GATA9 |
| IAA33 | 16.14505 | 2.04325474 | 0.00000000 | 2.32842379 | 2.10530848 | 0.09034905 | 0.00000000 | 0.00000000 | 0.006524618 | 4.999287 | 4.571902 | AT5G57420 | 4.43373226 | 2.04325474 | 0.09687367 | 7.042542 | 9.571189 | IAA33 |
| AT4G39780 | 14.44201 | 0.14693288 | 2.32588378 | 0.25525715 | 0.52792938 | 0.00000000 | 0.03160388 | 0.22926870 | 1.693975779 | 3.570388 | 5.660773 | AT4G39780 | 0.78318653 | 2.47281667 | 1.95484836 | 6.043205 | 9.231161 | AT4G39780 |
| HSFA7A | 12.76367 | 0.25117755 | 0.00000000 | 0.12291169 | 0.14593023 | 1.12909171 | 0.57757904 | 0.02587089 | 1.529779244 | 3.302799 | 5.678535 | AT3G51910 | 0.26884192 | 0.25117755 | 3.26232088 | 3.553977 | 8.981335 | HSFA7A |
| IAA1 | 13.99014 | 3.44843783 | 1.03729841 | 0.35891422 | 0.00000000 | 0.00000000 | 0.00000000 | 0.17173008 | 0.000000000 | 6.512181 | 2.461580 | AT4G14560 | 0.35891422 | 4.48573624 | 0.17173008 | 10.997917 | 8.973761 | IAA1 |
| AIL5 | 10.38088 | 0.09034607 | 0.00000000 | 0.16107411 | 0.00000000 | 0.00000000 | 0.30926547 | 0.11470664 | 0.763348449 | 4.423238 | 4.518897 | AT5G57390 | 0.16107411 | 0.09034607 | 1.18732056 | 4.513585 | 8.942136 | AIL5 |
| CZF1 | 15.29904 | 0.08482487 | 0.00000000 | 0.01961690 | 0.20612989 | 2.08046485 | 2.36262858 | 0.31965426 | 1.518278929 | 2.362619 | 6.344818 | AT2G40140 | 0.22574680 | 0.08482487 | 6.28102662 | 2.447444 | 8.707437 | CZF1 |
| RAV2 | 16.97841 | 0.00000000 | 0.00000000 | 0.03886414 | 0.01622127 | 1.19765425 | 3.28355107 | 1.80566397 | 1.941675572 | 3.559780 | 5.134996 | AT1G68840 | 0.05508541 | 0.00000000 | 8.22854486 | 3.559780 | 8.694776 | RAV2 |
| SPT | 12.67462 | 0.52587389 | 0.00000000 | 1.19645196 | 0.30685557 | 0.21419357 | 0.27815884 | 1.72415211 | 0.110248577 | 4.064805 | 4.253880 | AT4G36930 | 1.50330752 | 0.52587389 | 2.32675310 | 4.590679 | 8.318685 | SPT |
| WRKY15 | 10.63751 | 0.47817392 | 0.02048728 | 1.02409457 | 0.29964036 | 0.00000000 | 0.31877359 | 0.86839409 | 0.077883400 | 4.348954 | 3.201104 | AT2G23320 | 1.32373494 | 0.49866120 | 1.26505108 | 4.847615 | 7.550058 | WRKY15 |
| FEZ | 13.19100 | 2.00246239 | 0.00000000 | 1.68698902 | 2.06792687 | 0.03503293 | 0.00000000 | 0.03390754 | 0.000000000 | 3.960380 | 3.404301 | AT1G26870 | 3.75491589 | 2.00246239 | 0.06894047 | 5.962842 | 7.364681 | FEZ |
| NAC060 | 13.90034 | 1.37457729 | 0.00000000 | 1.38061300 | 0.55627773 | 0.05821980 | 0.00000000 | 3.22494352 | 0.000000000 | 2.937602 | 4.368105 | AT3G44290 | 1.93689074 | 1.37457729 | 3.28316332 | 4.312180 | 7.305707 | NAC060 |
| GATA7 | 13.48570 | 1.70655646 | 0.00000000 | 2.24901955 | 1.78833878 | 0.13212587 | 0.00000000 | 0.32314648 | 0.000000000 | 3.447756 | 3.838753 | AT4G36240 | 4.03735833 | 1.70655646 | 0.45527235 | 5.154313 | 7.286509 | GATA7 |
| AGL21 | 12.99762 | 0.30577133 | 0.00000000 | 1.28488660 | 1.95284560 | 1.05455084 | 0.57902455 | 0.17265673 | 0.395315424 | 1.657627 | 5.594945 | AT4G37940 | 3.23773220 | 0.30577133 | 2.20154754 | 1.963398 | 7.252572 | AGL21 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| AT2G44430 | 1.1433335 | 0.072991227 | 0.000000000 | 0.055361679 | 0.10355717 | 0.000000000 | 0.042612652 | 0.131317893 | 0.002758878 | 0.28816844 | 0.44656555 | AT2G44430 | 0.15891885 | 0.072991227 | 0.176689423 | 0.36115967 | 0.7347340 | AT2G44430 |
| ATL9 | 1.1733175 | 0.288751722 | 0.090914616 | 0.045555083 | 0.01310015 | 0.000000000 | 0.000000000 | 0.009491084 | 0.000000000 | 0.45508627 | 0.27041862 | AT2G35000 | 0.05865523 | 0.379666338 | 0.009491084 | 0.83475261 | 0.7255049 | ATL9 |
| AT4G00990 | 1.1372743 | 0.059034957 | 0.000000000 | 0.024056438 | 0.07713330 | 0.058688200 | 0.072908615 | 0.035123688 | 0.091932576 | 0.29453820 | 0.42385828 | AT4G00990 | 0.10118974 | 0.059034957 | 0.258653080 | 0.35357316 | 0.7183965 | AT4G00990 |
| AT4G22360 | 1.1906707 | 0.132351821 | 0.022326744 | 0.042387773 | 0.13554284 | 0.000000000 | 0.052304850 | 0.075008821 | 0.090236664 | 0.29358937 | 0.34692181 | AT4G22360 | 0.17793061 | 0.154678566 | 0.217550336 | 0.44826794 | 0.6405112 | AT4G22360 |
| AT1G77570 | 1.2236760 | 0.041163811 | 0.000000000 | 0.006462979 | 0.15040066 | 0.107727837 | 0.095905310 | 0.070494384 | 0.129695670 | 0.22817905 | 0.39364634 | AT1G77570 | 0.15686363 | 0.041163811 | 0.403823201 | 0.26934286 | 0.6218254 | AT1G77570 |
| AP2 | 1.2191948 | 0.019043956 | 0.000000000 | 0.399088794 | 0.13243400 | 0.000000000 | 0.019881790 | 0.011947627 | 0.015291155 | 0.35659194 | 0.26491557 | AT4G36920 | 0.53152279 | 0.019043956 | 0.047120571 | 0.37563589 | 0.6215075 | AP2 |
| HB4 | 1.1705773 | 0.041553499 | 0.017786153 | 0.501374901 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.56494678 | 0.04491594 | AT2G44910 | 0.50137490 | 0.059339653 | 0.000000000 | 0.62428643 | 0.6098627 | HB4 |
| emb2746 | 1.1474762 | 0.182308552 | 0.050665384 | 0.113081277 | 0.05885296 | 0.111362475 | 0.000000000 | 0.000000000 | 0.024297435 | 0.43420448 | 0.17270365 | AT5G63420 | 0.17193424 | 0.232973936 | 0.135659910 | 0.66717842 | 0.6069081 | emb2746 |
| SNL6 | 0.8550012 | 0.023659907 | 0.000000000 | 0.063431008 | 0.00503883 | 0.073257357 | 0.066434647 | 0.020631980 | 0.000000000 | 0.23270605 | 0.36984142 | AT1G10450 | 0.06846984 | 0.023659907 | 0.160323984 | 0.25636596 | 0.6025475 | SNL6 |
| AT1G29560 | 1.0315696 | 0.000000000 | 0.000000000 | 0.000000000 | 0.00000000 | 0.018770469 | 0.135203956 | 0.202456494 | 0.084445437 | 0.10774930 | 0.48294394 | AT1G29560 | 0.00000000 | 0.000000000 | 0.440876356 | 0.10774930 | 0.5906932 | AT1G29560 |
| HDG12 | 0.9730190 | 0.218143183 | 0.093345897 | 0.106459481 | 0.00000000 | 0.000000000 | 0.007542114 | 0.005922467 | 0.000000000 | 0.21777559 | 0.32383023 | AT1G17920 | 0.10645948 | 0.311489080 | 0.013464581 | 0.52926467 | 0.5416058 | HDG12 |
| ATX1 | 0.8864589 | 0.057411332 | 0.000000000 | 0.000000000 | 0.01786961 | 0.000000000 | 0.107965800 | 0.088133401 | 0.088810452 | 0.33965662 | 0.18661166 | AT2G31650 | 0.01786961 | 0.057411332 | 0.284909654 | 0.39706796 | 0.5262683 | ATX1 |
| SDG2 | 0.9271838 | 0.075570925 | 0.000000000 | 0.064197352 | 0.05093946 | 0.019516134 | 0.095844542 | 0.038380871 | 0.057095350 | 0.16208961 | 0.36354955 | AT4G15180 | 0.11513681 | 0.075570925 | 0.210836897 | 0.23766053 | 0.5256392 | SDG2 |
| BIM2 | 0.9361748 | 0.163722345 | 0.012186476 | 0.056697001 | 0.03083672 | 0.013640166 | 0.106207830 | 0.000000000 | 0.059772613 | 0.37539485 | 0.11771680 | AT1G69010 | 0.08753372 | 0.175908821 | 0.179620610 | 0.55130368 | 0.4931117 | BIM2 |
| AT1G19490 | 0.7099215 | 0.021325098 | 0.000000000 | 0.000000000 | 0.00300631 | 0.000000000 | 0.000000000 | 0.196437363 | 0.000000000 | 0.26760075 | 0.22155199 | AT1G19490 | 0.00300631 | 0.021325098 | 0.196437363 | 0.28892585 | 0.4891527 | AT1G19490 |
| AT5G61190 | 0.7849813 | 0.000000000 | 0.000000000 | 0.021986320 | 0.01516794 | 0.037719845 | 0.104285243 | 0.026196341 | 0.091470999 | 0.11023629 | 0.37791828 | AT5G61190 | 0.03715426 | 0.000000000 | 0.259672428 | 0.11023629 | 0.4881546 | AT5G61190 |
| TTG1 | 0.8123302 | 0.055661500 | 0.012133827 | 0.004667502 | 0.15792420 | 0.009228484 | 0.000000000 | 0.024511383 | 0.095730808 | 0.30142991 | 0.15104256 | AT5G24520 | 0.16259170 | 0.067795326 | 0.129470676 | 0.36922524 | 0.4524725 | TTG1 |
| AT5G65490 | 0.8501531 | 0.059380136 | 0.000000000 | 0.108671858 | 0.03655478 | 0.099894416 | 0.072065012 | 0.020631980 | 0.011682464 | 0.17726689 | 0.26400554 | AT5G65490 | 0.14522664 | 0.059380136 | 0.204273873 | 0.23664703 | 0.4412724 | AT5G65490 |
| AT1G50410 | 0.6801054 | 0.016592083 | 0.054959221 | 0.032585993 | 0.05737101 | 0.010582344 | 0.055800816 | 0.000000000 | 0.056503739 | 0.11084198 | 0.28486824 | AT1G50410 | 0.08995700 | 0.071551304 | 0.122886899 | 0.18239329 | 0.3957102 | AT1G50410 |
| AT4G14720 | 0.7485349 | 0.090064565 | 0.000000000 | 0.000000000 | 0.02046105 | 0.040876028 | 0.060330614 | 0.079824766 | 0.066441335 | 0.07793966 | 0.31259686 | AT4G14720 | 0.02046105 | 0.090064565 | 0.247472743 | 0.16800423 | 0.3905365 | AT4G14720 |
| AT1G06070 | 0.6334994 | 0.050735542 | 0.000000000 | 0.014475954 | 0.02544888 | 0.011751935 | 0.080401744 | 0.050272404 | 0.015356323 | 0.20628562 | 0.17877098 | AT1G06070 | 0.03992483 | 0.050735542 | 0.157782406 | 0.25702116 | 0.3850566 | AT1G06070 |
| SUVH6 | 0.6498734 | 0.000000000 | 0.008874459 | 0.037230484 | 0.04069710 | 0.061234753 | 0.164587763 | 0.007745382 | 0.000000000 | 0.05289082 | 0.27661259 | AT2G22740 | 0.07792758 | 0.008874459 | 0.233567898 | 0.06176528 | 0.3295034 | SUVH6 |
| AT4G29000 | 0.5451469 | 0.000000000 | 0.037445887 | 0.019616904 | 0.03167117 | 0.024086421 | 0.038167104 | 0.033994581 | 0.039343211 | 0.10132238 | 0.21949921 | AT4G29000 | 0.05128807 | 0.037445887 | 0.135591316 | 0.13876827 | 0.3208216 | AT4G29000 |
| HDG11 | 0.5311721 | 0.000000000 | 0.000000000 | 0.000000000 | 0.19711722 | 0.000000000 | 0.027767186 | 0.009034042 | 0.000000000 | 0.11466475 | 0.18258891 | AT1G73360 | 0.19711722 | 0.000000000 | 0.036801229 | 0.11466475 | 0.2972537 | HDG11 |
| AT1G80400 | 0.4368548 | 0.000000000 | 0.000000000 | 0.041609381 | 0.07850888 | 0.005875967 | 0.046036440 | 0.000000000 | 0.026607122 | 0.07772135 | 0.16049571 | AT1G80400 | 0.12011826 | 0.000000000 | 0.078519529 | 0.07772135 | 0.2382171 | AT1G80400 |
| AT5G41580 | 0.4184683 | 0.042952453 | 0.056138826 | 0.030032002 | 0.02211479 | 0.011751935 | 0.000000000 | 0.000000000 | 0.026072789 | 0.15317763 | 0.07622784 | AT5G41580 | 0.05214679 | 0.099091279 | 0.037824724 | 0.25226891 | 0.2294055 | AT5G41580 |
| SPL11 | 0.4367499 | 0.020581905 | 0.000000000 | 0.000000000 | 0.01180297 | 0.000000000 | 0.018617567 | 0.000000000 | 0.157792818 | 0.04504666 | 0.18290793 | AT1G27360 | 0.01180297 | 0.020581905 | 0.176410386 | 0.06562856 | 0.2279546 | SPL11 |
| IBM1 | 0.3705567 | 0.063398444 | 0.000000000 | 0.018846880 | 0.02859987 | 0.015372763 | 0.031280611 | 0.009034042 | 0.005491118 | 0.05245095 | 0.14608204 | AT3G07610 | 0.04744675 | 0.063398444 | 0.061178535 | 0.11584939 | 0.1985330 | IBM1 |
| AT5G58340 | 0.2733469 | 0.000000000 | 0.017099567 | 0.000000000 | 0.02228929 | 0.000000000 | 0.003837322 | 0.034610546 | 0.036940482 | 0.02888053 | 0.12968914 | AT5G58340 | 0.02228929 | 0.017099567 | 0.075388350 | 0.04598010 | 0.1585697 | AT5G58340 |
| AT3G04450 | 0.1511582 | 0.004281654 | 0.024905136 | 0.000000000 | 0.00000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.000000000 | 0.06528348 | 0.05668791 | AT3G04450 | 0.00000000 | 0.029186790 | 0.000000000 | 0.09447027 | 0.1219714 | AT3G04450 |
In [292]:
rc_rank[1:50,]
| all | atri | tri | cor | end | per | pro | xyl | phl | lrc | col | GeneID | ground | epi | stele | epilrc | rc | GeneName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | |
| BT2 | 24.625115 | 3.63243231 | 1.11023217 | 0.29313509 | 0.02987186 | 0.000000000 | 0.00000000 | 0.02836394 | 0.000000000 | 11.334815 | 8.196265 | AT3G48360 | 0.32300695 | 4.74266448 | 0.02836394 | 16.077479 | 19.531080 | BT2 |
| NAI1 | 23.015895 | 3.59541458 | 0.94266601 | 0.29111093 | 0.09984688 | 0.000000000 | 0.00000000 | 0.03480724 | 0.000000000 | 9.114967 | 8.937082 | AT2G22770 | 0.39095781 | 4.53808059 | 0.03480724 | 13.653048 | 18.052049 | NAI1 |
| LBD15 | 19.768097 | 0.07609731 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.00000000 | 2.40501651 | 0.000000000 | 9.390180 | 7.896803 | AT2G40470 | 0.00000000 | 0.07609731 | 2.40501651 | 9.466278 | 17.286983 | LBD15 |
| SMB | 20.512964 | 2.60418556 | 0.00000000 | 1.68861519 | 0.21402041 | 0.000000000 | 0.00000000 | 0.00000000 | 0.000000000 | 8.761212 | 7.244931 | AT1G79580 | 1.90263560 | 2.60418556 | 0.00000000 | 11.365398 | 16.006143 | SMB |
| AT1G36060 | 23.062578 | 4.42372479 | 1.20398150 | 1.65039467 | 0.17359875 | 0.000000000 | 0.00000000 | 0.06677439 | 0.000000000 | 6.478396 | 9.065708 | AT1G36060 | 1.82399342 | 5.62770628 | 0.06677439 | 12.106102 | 15.544104 | AT1G36060 |
| AIL6 | 15.270310 | 0.41155982 | 0.00000000 | 0.29413650 | 0.17894253 | 0.078612562 | 0.00000000 | 0.05208743 | 0.000000000 | 7.417725 | 6.837246 | AT5G10510 | 0.47307903 | 0.41155982 | 0.13069999 | 7.829285 | 14.254971 | AIL6 |
| CRF3 | 21.977124 | 2.35888401 | 2.33674161 | 0.50177245 | 1.74067162 | 1.005704681 | 0.10547801 | 0.00000000 | 0.120318333 | 8.713660 | 5.093893 | AT5G53290 | 2.24244407 | 4.69562562 | 1.23150102 | 13.409286 | 13.807553 | CRF3 |
| BRON | 19.433912 | 3.19499965 | 0.12943552 | 1.36769378 | 1.56110729 | 0.039504121 | 0.00000000 | 0.00000000 | 0.000000000 | 8.324952 | 4.816220 | AT1G75710 | 2.92880107 | 3.32443516 | 0.03950412 | 11.649387 | 13.141172 | BRON |
| PLT1 | 22.457565 | 3.59503298 | 0.00000000 | 2.35070851 | 2.47832175 | 0.151840493 | 0.05937524 | 0.86518002 | 0.000000000 | 6.550585 | 6.406522 | AT3G20840 | 4.82903026 | 3.59503298 | 1.07639575 | 10.145618 | 12.957106 | PLT1 |
| AT1G26680 | 15.492356 | 0.05400877 | 0.00000000 | 0.99504250 | 1.53634929 | 0.071892083 | 0.00000000 | 0.00000000 | 0.000000000 | 5.968598 | 6.866465 | AT1G26680 | 2.53139179 | 0.05400877 | 0.07189208 | 6.022607 | 12.835063 | AT1G26680 |
| ZFP5 | 23.600817 | 2.60660283 | 0.09155688 | 0.02023301 | 0.00000000 | 0.000000000 | 2.74638834 | 4.84269017 | 0.944938866 | 5.417175 | 6.931232 | AT1G10480 | 0.02023301 | 2.69815971 | 8.53401737 | 8.115335 | 12.348407 | ZFP5 |
| AT1G32700 | 19.460244 | 0.00000000 | 0.00000000 | 0.00000000 | 0.08488294 | 1.370845666 | 1.79691876 | 1.54955888 | 2.941386341 | 3.439036 | 8.277616 | AT1G32700 | 0.08488294 | 0.00000000 | 7.65870965 | 3.439036 | 11.716652 | AT1G32700 |
| NAC094 | 15.747321 | 1.88038954 | 0.00000000 | 1.56546165 | 1.19488706 | 0.000000000 | 0.00000000 | 0.00000000 | 0.000000000 | 5.291631 | 5.814952 | AT5G39820 | 2.76034871 | 1.88038954 | 0.00000000 | 7.172020 | 11.106582 | NAC094 |
| EEL | 15.393120 | 1.81503913 | 0.00000000 | 1.99649730 | 0.25629653 | 0.184404726 | 0.06875881 | 0.15174492 | 0.000000000 | 7.133394 | 3.786984 | AT2G41070 | 2.25279383 | 1.81503913 | 0.40490846 | 8.948434 | 10.920379 | EEL |
| PLT2 | 17.036736 | 1.89290722 | 0.44054300 | 2.23299921 | 2.04107694 | 0.015921570 | 0.00000000 | 0.02506736 | 0.000000000 | 4.121808 | 6.266412 | AT1G51190 | 4.27407615 | 2.33345021 | 0.04098893 | 6.455259 | 10.388220 | PLT2 |
| AT1G22190 | 15.637424 | 3.89960985 | 0.12121212 | 0.77372647 | 0.13888586 | 0.000000000 | 0.05192027 | 0.28787218 | 0.060517590 | 5.741278 | 4.562402 | AT1G22190 | 0.91261233 | 4.02082197 | 0.40031004 | 9.762100 | 10.303680 | AT1G22190 |
| GATA9 | 16.978013 | 1.63907316 | 0.02511891 | 0.72781165 | 0.49645267 | 0.118567150 | 0.04010268 | 3.82489668 | 0.064742300 | 4.216369 | 5.824879 | AT4G32890 | 1.22426432 | 1.66419207 | 4.04830882 | 5.880561 | 10.041248 | GATA9 |
| IAA33 | 16.145050 | 2.04325474 | 0.00000000 | 2.32842379 | 2.10530848 | 0.090349051 | 0.00000000 | 0.00000000 | 0.006524618 | 4.999287 | 4.571902 | AT5G57420 | 4.43373226 | 2.04325474 | 0.09687367 | 7.042542 | 9.571189 | IAA33 |
| AT4G39780 | 14.442012 | 0.14693288 | 2.32588378 | 0.25525715 | 0.52792938 | 0.000000000 | 0.03160388 | 0.22926870 | 1.693975779 | 3.570388 | 5.660773 | AT4G39780 | 0.78318653 | 2.47281667 | 1.95484836 | 6.043205 | 9.231161 | AT4G39780 |
| HSFA7A | 12.763675 | 0.25117755 | 0.00000000 | 0.12291169 | 0.14593023 | 1.129091710 | 0.57757904 | 0.02587089 | 1.529779244 | 3.302799 | 5.678535 | AT3G51910 | 0.26884192 | 0.25117755 | 3.26232088 | 3.553977 | 8.981335 | HSFA7A |
| IAA1 | 13.990141 | 3.44843783 | 1.03729841 | 0.35891422 | 0.00000000 | 0.000000000 | 0.00000000 | 0.17173008 | 0.000000000 | 6.512181 | 2.461580 | AT4G14560 | 0.35891422 | 4.48573624 | 0.17173008 | 10.997917 | 8.973761 | IAA1 |
| AIL5 | 10.380876 | 0.09034607 | 0.00000000 | 0.16107411 | 0.00000000 | 0.000000000 | 0.30926547 | 0.11470664 | 0.763348449 | 4.423238 | 4.518897 | AT5G57390 | 0.16107411 | 0.09034607 | 1.18732056 | 4.513585 | 8.942136 | AIL5 |
| CZF1 | 15.299035 | 0.08482487 | 0.00000000 | 0.01961690 | 0.20612989 | 2.080464849 | 2.36262858 | 0.31965426 | 1.518278929 | 2.362619 | 6.344818 | AT2G40140 | 0.22574680 | 0.08482487 | 6.28102662 | 2.447444 | 8.707437 | CZF1 |
| RAV2 | 16.978406 | 0.00000000 | 0.00000000 | 0.03886414 | 0.01622127 | 1.197654247 | 3.28355107 | 1.80566397 | 1.941675572 | 3.559780 | 5.134996 | AT1G68840 | 0.05508541 | 0.00000000 | 8.22854486 | 3.559780 | 8.694776 | RAV2 |
| SPT | 12.674619 | 0.52587389 | 0.00000000 | 1.19645196 | 0.30685557 | 0.214193575 | 0.27815884 | 1.72415211 | 0.110248577 | 4.064805 | 4.253880 | AT4G36930 | 1.50330752 | 0.52587389 | 2.32675310 | 4.590679 | 8.318685 | SPT |
| WRKY15 | 10.637505 | 0.47817392 | 0.02048728 | 1.02409457 | 0.29964036 | 0.000000000 | 0.31877359 | 0.86839409 | 0.077883400 | 4.348954 | 3.201104 | AT2G23320 | 1.32373494 | 0.49866120 | 1.26505108 | 4.847615 | 7.550058 | WRKY15 |
| FEZ | 13.191000 | 2.00246239 | 0.00000000 | 1.68698902 | 2.06792687 | 0.035032933 | 0.00000000 | 0.03390754 | 0.000000000 | 3.960380 | 3.404301 | AT1G26870 | 3.75491589 | 2.00246239 | 0.06894047 | 5.962842 | 7.364681 | FEZ |
| NAC060 | 13.900339 | 1.37457729 | 0.00000000 | 1.38061300 | 0.55627773 | 0.058219803 | 0.00000000 | 3.22494352 | 0.000000000 | 2.937602 | 4.368105 | AT3G44290 | 1.93689074 | 1.37457729 | 3.28316332 | 4.312180 | 7.305707 | NAC060 |
| GATA7 | 13.485697 | 1.70655646 | 0.00000000 | 2.24901955 | 1.78833878 | 0.132125871 | 0.00000000 | 0.32314648 | 0.000000000 | 3.447756 | 3.838753 | AT4G36240 | 4.03735833 | 1.70655646 | 0.45527235 | 5.154313 | 7.286509 | GATA7 |
| AGL21 | 12.997623 | 0.30577133 | 0.00000000 | 1.28488660 | 1.95284560 | 1.054550842 | 0.57902455 | 0.17265673 | 0.395315424 | 1.657627 | 5.594945 | AT4G37940 | 3.23773220 | 0.30577133 | 2.20154754 | 1.963398 | 7.252572 | AGL21 |
| WRKY9 | 13.852944 | 3.62955559 | 3.04326707 | 0.00000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.000000000 | 5.227408 | 1.952713 | AT1G68150 | 0.00000000 | 6.67282266 | 0.00000000 | 11.900231 | 7.180121 | WRKY9 |
| GATA5 | 12.572411 | 1.32253115 | 0.07840308 | 1.56135148 | 0.31408409 | 0.082172399 | 0.06539380 | 2.16904893 | 0.222794805 | 2.904670 | 3.851962 | AT5G66320 | 1.87543557 | 1.40093423 | 2.53940993 | 4.305604 | 6.756631 | GATA5 |
| LBD4 | 7.864280 | 0.57471249 | 0.00000000 | 0.26084583 | 0.25303588 | 0.043634416 | 0.01120512 | 0.01727922 | 0.048576210 | 3.699602 | 2.955389 | AT1G31320 | 0.51388171 | 0.57471249 | 0.12069497 | 4.274315 | 6.654991 | LBD4 |
| ANL2 | 10.045988 | 2.46913395 | 0.16400986 | 1.51586594 | 0.23474477 | 0.000000000 | 0.00000000 | 0.00000000 | 0.000000000 | 3.823830 | 1.838403 | AT4G00730 | 1.75061072 | 2.63314382 | 0.00000000 | 6.456974 | 5.662234 | ANL2 |
| TLP6 | 9.170788 | 0.99195344 | 0.00000000 | 1.76455896 | 0.18036297 | 0.065039451 | 0.19557072 | 0.29448552 | 0.042768440 | 2.555259 | 3.080790 | AT1G47270 | 1.94492193 | 0.99195344 | 0.59786412 | 3.547213 | 5.636049 | TLP6 |
| WRKY31 | 9.079241 | 1.80595385 | 1.55188666 | 0.15591249 | 0.00000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.000000000 | 1.203903 | 4.361585 | AT4G22070 | 0.15591249 | 3.35784051 | 0.00000000 | 4.561743 | 5.565488 | WRKY31 |
| AT1G21000 | 8.514529 | 0.19687575 | 0.00000000 | 0.05848105 | 0.02670898 | 0.752568658 | 1.10338234 | 0.29714627 | 0.564318157 | 2.467871 | 3.047177 | AT1G21000 | 0.08519002 | 0.19687575 | 2.71741542 | 2.664747 | 5.515048 | AT1G21000 |
| COL5 | 6.628381 | 0.45028555 | 0.25502893 | 0.05455954 | 0.07008941 | 0.033651889 | 0.18719860 | 0.13598704 | 0.064790003 | 2.936310 | 2.440480 | AT5G57660 | 0.12464895 | 0.70531449 | 0.42162753 | 3.641625 | 5.376790 | COL5 |
| BIM1 | 9.689638 | 1.54457500 | 0.49614988 | 0.65311551 | 0.12857946 | 0.677378152 | 0.42213528 | 0.06999108 | 0.361471855 | 2.284607 | 3.051636 | AT5G08130 | 0.78169496 | 2.04072488 | 1.53097636 | 4.325332 | 5.336242 | BIM1 |
| AT1G49475 | 7.863756 | 1.34378392 | 0.00000000 | 0.53272203 | 0.37476768 | 0.265463410 | 0.02057114 | 0.00000000 | 0.000000000 | 1.719020 | 3.607428 | AT1G49475 | 0.90748971 | 1.34378392 | 0.28603455 | 3.062804 | 5.326448 | AT1G49475 |
| AT5G48560 | 6.780565 | 0.81715633 | 0.03232323 | 0.12492085 | 0.13343679 | 0.110214386 | 0.19074536 | 0.00000000 | 0.101236657 | 1.935264 | 3.335268 | AT5G48560 | 0.25835765 | 0.84947956 | 0.40219641 | 2.784743 | 5.270532 | AT5G48560 |
| AT1G11950 | 7.435701 | 0.18341438 | 0.09074876 | 0.52698179 | 0.08044622 | 0.149614650 | 0.08453982 | 0.88819704 | 0.168801797 | 1.976938 | 3.286019 | AT1G11950 | 0.60742801 | 0.27416313 | 1.29115331 | 2.251101 | 5.262957 | AT1G11950 |
| RGL2 | 7.178475 | 0.32439784 | 0.00000000 | 0.76945112 | 0.33524994 | 0.096924103 | 0.16899200 | 0.08396658 | 0.180624707 | 3.543752 | 1.675116 | AT3G03450 | 1.10470105 | 0.32439784 | 0.53050739 | 3.868150 | 5.218868 | RGL2 |
| AT1G76580 | 5.077410 | 0.03522144 | 0.00000000 | 0.01477429 | 0.02499984 | 0.008613033 | 0.05217183 | 0.00000000 | 0.000000000 | 2.420307 | 2.521322 | AT1G76580 | 0.03977413 | 0.03522144 | 0.06078487 | 2.455529 | 4.941629 | AT1G76580 |
| PPD1 | 8.359987 | 1.15751408 | 0.08877131 | 1.61211302 | 0.29680443 | 0.325263880 | 0.05010427 | 0.05663241 | 0.027876254 | 3.185623 | 1.559284 | AT4G14713 | 1.90891745 | 1.24628539 | 0.45987681 | 4.431908 | 4.744907 | PPD1 |
| AT4G25400 | 7.009097 | 0.82815953 | 0.76312303 | 0.01974013 | 0.11955183 | 0.085079695 | 0.50513548 | 0.00000000 | 0.142685122 | 1.751770 | 2.793852 | AT4G25400 | 0.13929195 | 1.59128257 | 0.73290030 | 3.343052 | 4.545622 | AT4G25400 |
| WRKY26 | 7.314470 | 0.05983189 | 0.00000000 | 0.03488408 | 0.00000000 | 0.115834972 | 0.94680466 | 0.31338989 | 1.437225703 | 2.200918 | 2.205581 | AT5G07100 | 0.03488408 | 0.05983189 | 2.81325523 | 2.260750 | 4.406499 | WRKY26 |
| BBX30 | 8.256358 | 0.52310122 | 0.00000000 | 0.59086417 | 0.59879635 | 0.009882879 | 0.44202240 | 1.72242166 | 0.067284633 | 1.808589 | 2.493395 | AT4G15248 | 1.18966053 | 0.52310122 | 2.24161157 | 2.331691 | 4.301985 | BBX30 |
| RAP2.4 | 7.612737 | 0.18653016 | 0.00000000 | 0.05741518 | 0.25248550 | 0.479395953 | 0.78096079 | 1.42174603 | 0.198875370 | 1.725310 | 2.510018 | AT1G78080 | 0.30990068 | 0.18653016 | 2.88097814 | 1.911840 | 4.235328 | RAP2.4 |
| NAC083 | 8.152416 | 0.45452445 | 0.00000000 | 0.17216556 | 1.28120186 | 0.858158629 | 0.59563327 | 0.16965981 | 0.401886807 | 1.479094 | 2.740092 | AT5G13180 | 1.45336742 | 0.45452445 | 2.02533852 | 1.933618 | 4.219185 | NAC083 |
In [293]:
options(repr.plot.width=6, repr.plot.height=4)
ggplot(rc_rank[1:10,], aes(x=reorder(GeneName, rc, decreasing = FALSE), y=rc)) + geom_point(size=4)+
labs(title="Root Cap-specific TF Prioritization",x="", y = "Combined centrality score")+
theme_classic()+
theme(axis.text.x = element_text(color = "black",
size = 16, angle = 0),
axis.text.y = element_text( color = "black",
size = 16, angle = 0),
plot.title = element_text(color="black", size=18, face="bold"),
axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()
In [294]:
write.csv(rc_rank,"Root_Cap_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)
In [295]:
tf_rank <- rc_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])
p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])
p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])
In [296]:
p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))
In [297]:
options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Root Cap", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))
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